[{"_1":2,"_439":-5,"_440":-5},"loaderData",{"_3":4,"_12":13},"root",{"_5":-5,"_6":7,"_8":9},"user","hasAccess",false,"h1bUi",{"_10":7,"_11":-5},"enabled","userId","routes/jobs",{"_14":15,"_15":16,"_94":43,"_95":96,"_100":-5,"_101":102},"kind","job",{"_17":18,"_19":20,"_21":22,"_23":24,"_25":26,"_27":28,"_29":-5,"_30":-5,"_31":32,"_34":35,"_36":37,"_38":37,"_39":-5,"_40":41,"_42":43,"_44":45,"_46":47,"_48":7,"_49":50,"_51":-5,"_52":53,"_55":56,"_58":-5,"_59":60,"_72":73,"_74":75,"_76":77,"_78":-5,"_79":-5,"_80":-5,"_81":-5,"_82":83,"_84":-5,"_85":7,"_86":87,"_88":7,"_89":90,"_91":-5,"_92":93},"id","50a416b8-29dc-4a1f-8de8-9621bc837bce","title","Senior AI & ML engineer","url","https://jobs.lever.co/ekimetrics/348c7a8c-61a7-4d9d-9c9e-34b90d488d38","job_description_full","","job_description_summary_short","Senior AI & ML Engineer role focused on artificial intelligence and machine learning engineering.","job_description_summary_long","The posting identifies a Senior AI & ML Engineer position but provides no additional description. Responsibilities, qualifications, technologies, benefits, work arrangement, and sponsorship details are not specified.","employment_type","compensation","seniority",[33],"senior","date_posted","2026-07-17T15:15:22.276+00:00","created_at","2026-09-05T02:03:20.131485+00:00","updated_at","deleted_at","company","Ekimetrics","company_slug","ekimetrics","profile_pic_url","https://wzishhuxscpbsvxosipt.supabase.co/storage/v1/object/public/company_logos/companies/a0fe83c2-94a1-4817-831e-420800f0556b.png","career_category","Software Engineering","sponsors_h1b","headcount_range","RANGE_501_1000","latest_funding_class","location_ids",[54],1250015082,"sub_categories",[57],"ML Engineering","tech_stack","locations",[61],{"_17":54,"_62":63,"_64":65,"_66":67,"_68":69,"_70":71},"city","Paris","slug","paris-fra","state","Île-de-France","country","France","country_iso3","FRA","requirements",[],"responsibilities",[],"benefits",[],"salary_minvalue","salary_maxvalue","salary_currency","salary_unitText","credential_category","no-requirements","months_of_experience","experience_in_place_of_education","home_page_url","https://ekimetrics.com","company_sponsors_h1b","h1b_sponsorship_status","unknown","h1b_sponsorship_evidence","company_id","a0fe83c2-94a1-4817-831e-420800f0556b","companySlug","companyInfo",{"_17":93,"_97":41,"_64":43,"_86":87,"_98":43,"_49":50,"_99":-5},"name","linkedin_slug","description","h1bHistory","similarJobs",[103,156,196,256,329,385],{"_17":104,"_19":105,"_21":106,"_23":107,"_25":108,"_27":109,"_29":-5,"_30":-5,"_31":110,"_34":111,"_36":112,"_38":113,"_39":-5,"_40":114,"_42":115,"_44":116,"_46":47,"_48":117,"_49":118,"_51":119,"_52":120,"_55":121,"_58":123,"_59":132,"_72":134,"_74":146,"_76":154,"_78":-5,"_79":-5,"_80":-5,"_81":-5,"_82":83,"_84":-5,"_85":7,"_86":155},"c4741d02-b130-432f-b2e0-53b0148bf5b7","Sr. Machine Learning Engineer, Foundation Models Inference - Cloud OS & Inference","https://jobs.apple.com/en-us/details/200681444","We are the Foundation Model Inference team within Cloud OS and AI Inference organization. We are on a mission to build the most highly performant, secure and private inference stack that powers Siri AI, Apple Intelligence and Apps that are powered with the largest foundation models. Our systems serve billions of queries daily across Siri AI, Apple Intelligence, Apple Search, Apple Music, Apple TV, App Store, iMessage, Photos, Camera, Spotlight & Safari, at remarkably low latency with every ounce of compute extracted from the hardware beneath them. We optimise language, vision, and speech models with billions of parameters using state-of-the-art techniques and ship them at Apple scale. This is a rare opportunity to directly shape how AI reaches billions of people worldwide.\n\n

Description

You will work at the intersection of research and production, partnering closely with the Foundation Model Research team and our external partners to bring cutting-edge model architectures from prototype to planetary-scale deployment. You will own hard problems in inference efficiency, hardware/software codesign, systems architecture, and tooling, and help set the technical direction for the engineers around you. This role sits within CloudOS and Private Cloud Compute (PCC) — Apple's purpose-built, privacy-preserving cloud infrastructure for AI workloads. PCC represents a first-of-its-kind approach to running foundation models in the cloud with verifiable privacy guarantees, and CloudOS is the systems foundation that makes it possible. You will be building and optimising inference systems on top of this infrastructure, working closely with platform and security teams to deliver both performance and trust at scale.\n\n

Minimum Qualifications

Experience leading complex ambiguous Machine learning projects end to end. \nProficiency in PyTorch or JAX \nExperience working with Inference frameworks\nExperienced in Python / Rust / Go lang or similar programming languages\nProficiency in deploying applications on cloud platforms (AWS, GCP or equivalent) using K8S and docker.\n\n

Preferred Qualifications

Hands-on experience with LLM inference stacks.\nWorking knowledge of GPU or TPU programming concepts.\nExperience building and operating high-throughput services at large distributed scale.\nExperience building productions systems in Go or Python.\nStrong knowledge of deep learning architectures including Transformers, encoder/decoder models, and multimodal variants.\nExperience with inference optimization frameworks such as TensorRT-LLM, vLLM, SGLang, TGI, or Nvidia Triton Server.\nMS in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related field","Build and optimize foundation-model inference systems that serve billions of queries across Apple’s products. This senior role combines machine learning, systems architecture, hardware/software co-design, and privacy-preserving cloud infrastructure at planetary scale.","The Senior Machine Learning Engineer will bring cutting-edge foundation-model architectures from research prototypes to large-scale production deployment. Responsibilities center on inference efficiency, systems architecture, tooling, hardware/software co-design, and operating high-throughput services within Apple’s CloudOS and Private Cloud Compute infrastructure. The role requires proficiency with PyTorch or JAX, inference frameworks, Python, Rust, or Go, and cloud deployment using Kubernetes and Docker. Preferred experience includes LLM inference stacks, GPU or TPU programming, distributed systems, deep learning architectures, and inference optimization frameworks. An MS in a relevant technical field is preferred, and the role offers direct impact on AI systems serving billions of users.",[33],"2026-09-02T12:18:25.04+00:00","2026-09-02T22:16:02.128109+00:00","2026-09-04T22:45:04.50471+00:00","Apple","apple","https://wzishhuxscpbsvxosipt.supabase.co/storage/v1/object/public/company_logos/companies/f43dc9ee-2658-4365-80d1-7d2fd4e22258.jpg",true,"RANGE_10000_PLUS","PUBLIC",[54],[122,57],"Backend",[124,125,126,127,128,129,130,131],"AWS","Docker","Go","Google Cloud Platform","Kubernetes","Python","PyTorch","Rust",[133],{"_17":54,"_62":63,"_64":65,"_66":67,"_68":69,"_70":71},[135,136,137,138,139,140,141,142,143,144,145],"Proficiency in PyTorch or JAX.","Experience working with inference frameworks.","Experience with Python, Rust, Go, or similar programming languages.","Proficiency deploying applications on cloud platforms such as AWS or GCP using Kubernetes and Docker.","Experience leading complex, ambiguous machine learning projects end to end.","Hands-on experience with LLM inference stacks is preferred.","Working knowledge of GPU or TPU programming concepts is preferred.","Experience building production systems in Go or Python is preferred.","Strong knowledge of deep learning architectures, including Transformers, encoder/decoder models, and multimodal variants, is preferred.","Experience with TensorRT-LLM, vLLM, SGLang, TGI, or Nvidia Triton Server is preferred.","An MS in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related field is preferred.",[147,148,149,150,151,152,153],"Lead complex, ambiguous machine learning projects end to end.","Bring foundation-model architectures from research prototypes to planetary-scale production deployment.","Own inference efficiency, hardware/software co-design, systems architecture, and tooling.","Build and optimize inference systems on Apple’s CloudOS and Private Cloud Compute infrastructure.","Partner with Foundation Model Research, external partners, platform teams, and security teams.","Build and operate high-throughput services at large distributed scale.","Help set technical direction for surrounding engineers.",[],"https://www.apple.com",{"_17":157,"_19":158,"_21":159,"_23":160,"_25":161,"_27":162,"_29":-5,"_30":-5,"_31":163,"_34":164,"_36":165,"_38":166,"_39":-5,"_40":167,"_42":168,"_44":169,"_46":47,"_48":117,"_49":170,"_51":119,"_52":171,"_55":172,"_58":173,"_59":174,"_72":176,"_74":183,"_76":189,"_78":-5,"_79":-5,"_80":-5,"_81":-5,"_82":83,"_84":-5,"_85":7,"_86":195},"aa370948-21d8-479e-bcf0-a0694e77f95e","Senior Machine Learning Engineer","https://criteo.wd3.myworkdayjobs.com/Criteo_Career_Site/job/Paris/Senior-Machine-Learning-Engineer_r21089","

What You'll Do:

Shape the Future of AI-Powered Advertising

At Criteo, AI is not a side project, it is at the heart of everything we build.

Within the Criteo AI Lab, you'll join a team of researchers, machine learning engineers, and scientists who turn cutting-edge ideas into real-world products used by millions of people every day. We explore new approaches, challenge industry assumptions, and bring innovations from research papers to production systems operating at global scale.

This is a unique opportunity to work at the intersection of scientific innovation, large-scale machine learning, and product impact.

What you'll do

As a Machine Learning Engineer, you'll help design the next generation of AI technologies powering Criteo's advertising platform.

You will:

Design, develop, and evaluate novel machine learning approaches to solve complex prediction, optimization, and recommendation problems.
Transform research ideas into robust production systems capable of operating on petabytes of data and serving real-time decisions at massive scale.
Partner closely with researchers, software engineers, product managers, and data scientists to bring innovative solutions from concept to impact.
Experiment, learn, and iterate through rigorous offline evaluation and large-scale online A/B testing.
Contribute to a culture of technical excellence by sharing knowledge, challenging existing approaches, and helping shape the future direction of our AI capabilities.

Why this role is unique?

You'll have the opportunity to:

Influence products used across the global digital advertising ecosystem.
See your ideas tested on billions of daily interactions.
Work with large-scale datasets and modern machine learning technologies.
Collaborate with internationally recognized experts in AI, machine learning, and distributed systems.
Balance scientific exploration with tangible business impact.

Who You Are:

Strong foundations in Machine Learning, Deep Learning, Statistics, or Applied Mathematics.
Experience building, training, and evaluating deep learning models in production environments.
Familiarity with modern approaches such as Transformers, representation learning, recommendation systems, sequence modeling, or generative AI.
Proficiency in Python and machine learning frameworks such as PyTorch.
Experience working with large-scale datasets and distributed training environments.
A strong experimental mindset, combining scientific rigor with a focus on measurable outcomes.
The ability to collaborate effectively across research and engineering teams.

Our Technical stack :

At Criteo AI Lab, you'll work on large-scale recommendation and prediction systems tackling challenges in personalization, ranking, optimization, and user understanding.

Our teams leverage:

Python and PyTorch for model development
Large-scale distributed training infrastructure and GPU computing
Petabyte-scale data processing platforms
Advanced online experimentation and A/B testing frameworks
Real-time inference systems operating under strict latency constraints
Modern MLOps practices to bring innovations from research to production

What makes this team unique is the opportunity to bridge research and production, transforming cutting-edge AI innovations into products serving billions of advertising opportunities every day.

We acknowledge that many candidates may not meet every single role requirement listed above. If your experience looks a little different from our requirements but you believe that you can still bring value to the role, we’d love to see your application!

Who We Are:

We’re Criteo, the Commerce Intelligence Platform. Criteo helps businesses turn shopper signals into commerce outcomes while delivering more relevant experiences for shoppers. We use proprietary commerce intelligence and AI decisioning to drive relevance for shoppers and performance for businesses.

At Criteo, our culture is as unique as it is diverse. From our offices across the globe or from the comfort of home, our 3,600 Criteos collaborate together to build an open, impactful, and forward-thinking environment.

We foster a workplace where everyone is valued, and employment decisions are based solely on skills, qualifications, and business needs—never on non-job-related factors or legally protected characteristics.

What We Offer:

🏢 Ways of working – Our hybrid model blends home with in-office experiences, making space for both. 
📈 Grow with us – Learning, mentorship & career development programs. 
💪 Your wellbeing matters – Health benefits, wellness perks & mental health support. 
🤝 A team that cares – Diverse, inclusive, and globally connected. 
💸 Fair pay & perks – Attractive salary, with performance-based rewards and family-friendly policies, plus the potential for equity depending on role and level. 

Additional benefits may vary depending on the country where you work and the nature of your employment with Criteo. 

","Join Criteo AI Lab as a Senior Machine Learning Engineer developing large-scale machine learning solutions for prediction, optimization, recommendation, and personalization in digital advertising. You’ll bridge cutting-edge research and production systems serving billions of daily advertising opportunities.","The role involves designing, training, evaluating, and productionizing machine learning and deep learning models for Criteo’s advertising platform. You will work with petabyte-scale datasets, distributed training environments, GPU computing, and real-time inference systems with strict latency requirements. The position partners closely with researchers, software engineers, product managers, and data scientists to turn research ideas into measurable product outcomes. Relevant areas include Transformers, representation learning, recommendation systems, sequence modeling, and generative AI. The role offers the opportunity to influence globally used products while working in an internationally recognized AI and machine learning team.",[33],"2026-08-27T00:00:00+00:00","2026-09-03T00:14:18.865626+00:00","2026-09-04T22:43:20.49022+00:00","Criteo","criteo","https://wzishhuxscpbsvxosipt.supabase.co/storage/v1/object/public/company_logos/companies/0b08de38-4b53-4a76-a08f-5bb1a0824ac5.svg","RANGE_1001_5000",[54],[57],[129,130],[175],{"_17":54,"_62":63,"_64":65,"_66":67,"_68":69,"_70":71},[177,178,179,180,181,182],"Strong foundations in machine learning, deep learning, statistics, or applied mathematics.","Experience building, training, and evaluating deep learning models in production environments.","Familiarity with Transformers, representation learning, recommendation systems, sequence modeling, or generative AI.","Proficiency in Python and machine learning frameworks such as PyTorch.","Experience working with large-scale datasets and distributed training environments.","Ability to apply scientific rigor, focus on measurable outcomes, and collaborate across research and engineering teams.",[184,185,186,187,188],"Design, develop, and evaluate machine learning approaches for prediction, optimization, recommendation, personalization, ranking, and user understanding.","Transform research ideas into robust production systems operating on petabytes of data and serving real-time decisions at massive scale.","Train and evaluate deep learning models and conduct rigorous offline evaluation and large-scale online A/B testing.","Collaborate with researchers, software engineers, product managers, and data scientists to deliver machine learning products.","Share knowledge, challenge existing approaches, and contribute to the technical direction of Criteo’s AI capabilities.",[190,191,192,193,194],"Hybrid work combining home and in-office experiences.","Learning, mentorship, and career development programs.","Health benefits, wellness perks, and mental health support.","Diverse, inclusive, and globally connected workplace.","Attractive salary, performance-based rewards, family-friendly policies, and potential equity depending on role and level; benefits vary by country and employment type.","http://www.criteo.com",{"_17":197,"_19":198,"_21":199,"_23":200,"_25":201,"_27":202,"_29":-5,"_30":-5,"_31":203,"_34":204,"_36":205,"_38":206,"_39":-5,"_40":207,"_42":208,"_44":209,"_46":47,"_48":7,"_49":50,"_51":-5,"_52":210,"_55":212,"_58":213,"_59":222,"_72":230,"_74":242,"_76":251,"_78":-5,"_79":-5,"_80":-5,"_81":-5,"_82":83,"_84":254,"_85":7,"_86":255},"de88e8d8-1040-473f-ae58-929eb100a911","Senior ML Engineer - Offline Team","https://jobs.ashbyhq.com/voodoo/c16d0de3-e5b7-4fb9-ae0e-a906a75e9b15","ABOUT VOODOO\n\nFounded in 2013, Voodoo is a tech company that creates mobile games and apps with a mission to entertain the world. Gathering 800 employees, 7 billion downloads, and over 200 million active users, Voodoo is the #3 mobile publisher worldwide in terms of downloads after Google and Meta. Our portfolio includes chart-topping games like Mob Control and Block Jam, alongside popular apps such as BeReal and Wizz.\n\n\n\n\nTEAM\n\nThe Engineering & Data team builds innovative tech products and platforms to support the impressive growth of their gaming and consumer apps which allow Voodoo to stay at the forefront of the mobile gaming industry.\n\nThe Voodoo Ad-Network is an autonomous product group of around 60 highly driven professionals with an ambitious mission: building top-tier ad network services. Our primary goal is to leverage Voodoo's massive first-party data ecosystem to optimize and scale monetization. We are in a rapid growth phase, expanding into new ventures such as opening to external inventory, penetrating the external advertiser market, and driving social network monetization following our recent acquisition of BeReal. To support this incredible trajectory and promising early results, we are scaling our team.\n\nThe Models team is a core element of the targeting performance. It leverages machine learning and a strong business understanding to directly impact the product's financial performance. It's composed of mostly senior Data Analysts, Analytics Engineers, Data Engineers and Data Scientists/ML Engineers that iterate together on finding and building the next performing iteration.\n\nThis role can be either Paris or Helsinki based and done in a hybrid setup.\n\n\n\n\nROLE\n\nWe're looking for a Senior ML Engineer to join our Models team. You will be joining a dedicated squad of Data Engineers, Data Scientists, and ML Engineers focused on building and maintaining the ML training infrastructure that powers our ad-targeting models from data preprocessing through model deployment to production monitoring. \n\nIn this role, you will be leading the following topics:\n\n - Architectural Ownership: Take end-to-end ownership of highly visible projects from ideation to production release. This includes feature scoping, timeline estimation, architecture design, and benchmarking new technologies.\n\n - Pipeline Engineering: Build and maintain quality data and ML pipelines to align with ever-evolving business and machine learning needs. Optimize training pipelines for performance, memory efficiency, and cost (e.g. spot instance strategies, efficient data loading, preprocessed artifact reuse).\n\n - Data Scientists Enablement: Enable Data Scientists to iterate faster by providing reusable, well-tested pipeline components (transformers, dataloaders, training utilities) and reviewing their contributions to shared code. Extend dataset capabilities: integrating new data sources, scaling feature windows, and increasing training data volumes without breaking pipeline constraints.\n\n - Deep Learning Development: Contribute to deep learning development: GPU workload orchestration, custom PyTorch training loops, and model architecture support.\n\n - ML Lifecycle & Reproducibility: Maintain reproducibility and consistency across the ML lifecycle: versioned configs, experiment tracking, and online-offline consistency tooling.\n\n - Scalability & Reliability: Collaborate with infrastructure teams on scalability — node pools, resource monitoring, CI/CD migrations. Participate in weekly rotation to triage and resolve alerts from Airflow, dbt, and related systems.\n\n - Agile Collaboration: Thrive in a fast-paced agile environment with rapid decision-making processes. You will collaborate daily with back-end developers, data scientists, infrastructure engineers, and product managers.\n\n - Mentorship & Team Culture: You will actively contribute to our engineering culture, share knowledge, and ensure every team member feels comfortable, supported, and empowered to grow in their role.\n\n\n\n\nPROFILE\n\nWe are looking for a Senior ML Engineer who deeply understands both the ML training lifecycle and the specific challenges of putting machine learning models into production at scale.\n\n - 5+ years minimum of experience as an ML Engineer or a similar role\n\n - End-to-End ML Ownership: Problem framing, baselines, experimentation, deployment, and iteration\n\n - Python Proficiency: Extensive knowledge for ML pipeline code: preprocessing, training/evaluation workflows, experiment utilities, and reproducible configs\n\n - Training Mechanics: Comfortable implementing training mechanics where needed (custom steps/metrics, dataloading patterns, performance-conscious preprocessing), not only notebook-level prototyping\n\n - ML Frameworks: Practical experience training and evaluating models with scikit-learn, LightGBM, PyTorch for deep learning\n\n - Performance Optimization: Experience optimizing memory usage during data preprocessing and model training\n\n - Experiment Management: Hyperparameter tuning, experiment tracking, and reproducible training (configs, seeds, versioning)\n\n - Cloud & Infrastructure: Experience with Amazon Web Services. Familiarity with scalability, reliability, and security topics\n\n - ML Production Awareness: Understanding of the challenges involved in running ML models in production (familiar with topics such as feature store, training-serving skew, etc.)\n\n - Model Serving: Experience with model serving infrastructure (real-time or batch inference, latency/throughput optimization) is a plus\n\n - Excellent communication skills in English\n\n\n\n\nOUR STACK\n\nPython · Scikit-learn · LightGBM · PyTorch · DBT · Spark · MLflow · Prometheus · Terraform · Airflow · Kubernetes · Amazon Web Services\n\n\n\n\nBENEFITS\n\n - Excellent benefits that will depend on the country you're based in","Senior ML Engineer joining Voodoo’s Models team to build and operate the training infrastructure behind large-scale ad-targeting models. The role combines ML pipeline engineering, deep learning development, production reliability, and enablement of data scientists.","The Senior ML Engineer will own highly visible ML infrastructure projects from architecture and scoping through production release. Responsibilities include building scalable data and training pipelines, optimizing memory and compute costs, supporting PyTorch workloads, and maintaining reproducible ML experimentation and deployment workflows. The role also covers model-serving awareness, online-offline consistency, infrastructure scalability, and alert response across systems such as Airflow and dbt. Candidates should have at least five years of ML engineering experience, strong Python skills, and practical experience with scikit-learn, LightGBM, PyTorch, and AWS. The position is based in Paris or Helsinki in a hybrid setup and includes collaboration with data scientists, data engineers, infrastructure engineers, backend developers, and product managers.",[33],"2026-08-18T11:03:16.825+00:00","2026-09-04T22:16:14.861716+00:00","2026-09-04T22:47:31.611533+00:00","Voodoo","voodoo","https://wzishhuxscpbsvxosipt.supabase.co/storage/v1/object/public/company_logos/companies/2b3d377f-d2f6-4422-96f4-59113f6a229d.png",[211,54],1246177997,[57],[214,215,124,216,128,217,218,219,129,130,220,221],"Apache Airflow","Apache Spark","dbt","LightGBM","MLflow","Prometheus","scikit-learn","Terraform",[223,229],{"_17":211,"_62":224,"_64":225,"_66":226,"_68":227,"_70":228},"Helsinki","helsinki-fin","Uusimaa","Finland","FIN",{"_17":54,"_62":63,"_64":65,"_66":67,"_68":69,"_70":71},[231,232,233,234,235,236,237,238,239,240,241],"At least 5 years of experience as an ML Engineer or in a similar role.","Experience owning the end-to-end ML lifecycle, including problem framing, experimentation, deployment, and iteration.","Extensive Python experience for preprocessing, training and evaluation workflows, experiment utilities, and reproducible configurations.","Ability to implement custom training mechanics, metrics, data-loading patterns, and performance-conscious preprocessing.","Practical experience training and evaluating models with scikit-learn, LightGBM, and PyTorch.","Experience optimizing memory usage during data preprocessing and model training.","Experience with hyperparameter tuning, experiment tracking, reproducible training, configuration management, seeds, and versioning.","Experience with Amazon Web Services and familiarity with scalability, reliability, and security topics.","Understanding of production ML challenges such as feature stores and training-serving skew.","Experience with real-time or batch model-serving infrastructure and latency or throughput optimization is a plus.","Excellent communication skills in English.",[243,244,245,246,247,248,249,250],"Own ML infrastructure projects end to end, including scoping, estimation, architecture, benchmarking, and production release.","Build and maintain data and machine learning pipelines, optimizing performance, memory usage, and cloud costs.","Develop reusable, tested pipeline components and enable data scientists to work effectively with shared code and datasets.","Contribute to deep learning development through GPU orchestration, custom PyTorch training loops, and model architecture support.","Maintain reproducible ML workflows with versioned configurations, experiment tracking, and online-offline consistency tooling.","Collaborate on scalability, node pools, resource monitoring, and CI/CD migrations.","Participate in weekly rotation to triage and resolve alerts from Airflow, dbt, and related systems.","Mentor teammates and contribute to a supportive engineering culture.",[252,253],"Benefits vary depending on whether the role is based in Paris or Helsinki.","Hybrid work setup.",60,"https://voodoo.io",{"_17":257,"_19":258,"_21":259,"_23":260,"_25":261,"_27":262,"_29":-5,"_30":-5,"_31":263,"_34":264,"_36":265,"_38":266,"_39":-5,"_40":267,"_42":268,"_44":269,"_46":47,"_48":117,"_49":270,"_51":271,"_52":272,"_55":277,"_58":279,"_59":280,"_72":303,"_74":310,"_76":317,"_78":-5,"_79":-5,"_80":-5,"_81":-5,"_82":327,"_84":-5,"_85":117,"_86":328},"d646ea6d-3a22-409e-8418-49510ae445d6","Member of Technical Staff, Multilingual","https://jobs.ashbyhq.com/cohere/a87be947-00f0-4a4c-a690-a4922f88f553","Who are we?\n\nCohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems.\n\nWe’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that.\n\nWe obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft.\n\nWe are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us!\n\n\n\nRole Overview:\n\nWe are looking for world-class research scientists and research engineers to build cutting-edge multilingual AI to serve the world!\n\nAt Cohere, we care deeply about building technologies that are broadly accessible and useful, regardless of language. As a Member of Technical Staff on the Multilingual team, you'll be at the forefront of advancing language models that serve the world. You’ll push the boundaries of what's possible in natural language processing. This isn't just a technical role—it's an opportunity to contribute to groundbreaking research that will define the next generation of AI.\n\n\n\n\nKey Responsibilities:\n\n - Lead the design and implementation of scalable solutions to improve multilingual LLM performance across a wide variety of skills – if we need it to make a great multilingual model, you can build it!\n\n - Is perfect for someone passionate about languages and AI, with a keen eye for detail and strong technical skills.\n\n - Offers the opportunity to contribute to cutting-edge language technology, making a global impact.\n\n - Requires a self-starter who can work independently and deliver results efficiently.\n\n - Publish research findings and contribute to academic discourse in top-tier venues\n\n - Mentor junior team members and shape global best practices in multilingual AI / NLP\n \n \n\nQualifications:\n\n - PhD in Computer Science, Linguistics, or related field (or equivalent experience)\n\n - Proven track record in large-scale data processing and ML pipeline development\n\n - Expert in Python and software engineering best practice\n\n - Deep understanding of multilingual data challenges and NLP fundamentals\n\n - Passion for advancing language technology and making global impact\n\n - Strong publication record or demonstrated research potential\n\n - Excellent communicator able to bridge technical and research communities\n \n \n\nNote:\n\n - This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role.\n\n - If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply.\n\n\nWe value and celebrate diversity and strive to create an inclusive work environment for all. We welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations request form and we will work together to meet your needs.\n\n\n\n\n\nFULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS:\n\n - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch.\n\n - Full health and dental benefits, including a separate budget for mental health.\n\n - RRSP matching, 401K, Pension Scheme.\n\n - 100% Parental Leave top-up for up to 6 months, for either parent.\n\n - Annual enrichment benefits:\n \n Arts & culture, fitness/wellness, quality time, and a workspace improvement credit.\n \n Education & learning stipend for conferences, courses, and coaching.\n\n - 6 weeks of paid vacation (30 working days!)\n\n - Budget for traveling to other offices if you are remote, plus an annual company offsite.\n\n\n\n\nHOW AND WHERE WE WORK:\n\n - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon.\n\n - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events.\n\n - For those not near an office: a co-working benefit so you can work alongside others in your city.\n\n - Everyone receives a $500 home office stipend to set up your workspace properly.\n \n \n\nIf any of the above doesn’t line up exactly with your experience, we still encourage you to apply. \n\n\nWe strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs.\n\n\n\nWe may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider.\n\n\n\nBeware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.","Join Cohere’s Multilingual team as a Member of Technical Staff building scalable solutions that advance multilingual language-model performance. This research-focused role combines machine learning engineering, natural language processing, publication, and mentorship.","The role involves designing and implementing scalable systems to improve multilingual LLM performance across a broad range of capabilities. The successful candidate will develop large-scale data-processing and ML pipelines, contribute to multilingual NLP research, and publish findings in top-tier venues. The position requires a PhD in Computer Science, Linguistics, or a related field, or equivalent experience, along with expert Python and software engineering skills. The role also includes mentoring junior team members and establishing best practices across multilingual AI and NLP. It can be performed remotely or from Cohere offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin, or Seoul.",[33],"2026-08-17T15:57:25.097+00:00","2026-06-02T10:07:32.205917+00:00","2026-09-04T22:46:48.770919+00:00","Cohere","cohere","https://wzishhuxscpbsvxosipt.supabase.co/storage/v1/object/public/company_logos/companies/15d608e1-0d76-4ed2-a59b-7220a68c4c17.png","RANGE_201_500","SERIES_D",[273,274,54,275,276],1124279679,1124586170,1826645935,1840034016,[278,57],"AI Research",[129],[281,287,291,292,298],{"_17":273,"_62":282,"_64":283,"_66":284,"_68":285,"_70":286},"Toronto","toronto-can","Ontario","Canada","CAN",{"_17":274,"_62":288,"_64":289,"_66":290,"_68":285,"_70":286},"Montréal","montr-al-can","Quebec",{"_17":54,"_62":63,"_64":65,"_66":67,"_68":69,"_70":71},{"_17":275,"_62":293,"_64":294,"_66":295,"_68":296,"_70":297},"London","london-gbr","London, City of","United Kingdom","GBR",{"_17":276,"_62":299,"_64":300,"_66":299,"_68":301,"_70":302},"New York","new-york-new-york","United States","USA",[304,305,306,307,308,309],"PhD in Computer Science, Linguistics, or a related field, or equivalent experience.","Proven track record in large-scale data processing and ML pipeline development.","Expertise in Python and software engineering best practices.","Deep understanding of multilingual data challenges and NLP fundamentals.","Strong publication record or demonstrated research potential.","Passion for advancing language technology and the ability to communicate across technical and research communities.",[311,312,313,314,315,316],"Design and implement scalable solutions to improve multilingual LLM performance.","Develop large-scale data-processing systems and ML pipelines.","Advance multilingual language technology and NLP capabilities.","Publish research findings and contribute to academic discourse in top-tier venues.","Mentor junior team members and shape global best practices in multilingual AI and NLP.","Work independently, communicate across technical and research communities, and deliver results efficiently.",[318,319,320,321,322,323,324,325,326],"Remote-friendly work arrangement with no minimum in-office requirement, or access to offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin, and Seoul.","Weekly lunch stipend of $75, £75, or equivalent local currency.","Full health and dental benefits with a separate mental-health budget.","RRSP matching, 401K, and pension scheme options.","100% parental-leave top-up for up to six months for either parent.","Annual enrichment benefits covering arts and culture, fitness and wellness, quality time, workspace improvements, education, conferences, courses, and coaching.","Six weeks of paid vacation, or 30 working days.","Remote-worker travel budget for visiting other offices and an annual company offsite.","Daily lunch program, snacks, community events, coworking benefit, and a $500 home-office stipend.","postgraduate-degree","https://cohere.com",{"_17":330,"_19":331,"_21":332,"_23":333,"_25":334,"_27":335,"_29":-5,"_30":-5,"_31":336,"_34":337,"_36":338,"_38":339,"_39":-5,"_40":340,"_42":341,"_44":342,"_46":47,"_48":7,"_49":50,"_51":-5,"_52":343,"_55":344,"_58":346,"_59":353,"_72":355,"_74":367,"_76":374,"_78":-5,"_79":-5,"_80":-5,"_81":-5,"_82":83,"_84":-5,"_85":7,"_86":384},"99b9fd26-b4ad-4040-b32b-d9b5670245b3","Senior Machine Learning Engineer (SOC)","https://job-boards.eu.greenhouse.io/proton/jobs/4870234101","Join Proton and build a better internet where privacy is the default\nAt Proton, we believe that privacy is a fundamental human right and the cornerstone of democracy. Since our inception in 2014, founded by a team of scientists from CERN, we have dedicated ourselves to providing free and open-source technology to millions worldwide, ensuring access to privacy, security, and freedom online.\nOur journey began with Proton Mail, the largest secure email service globally, and has since expanded to include Proton VPN, Proton Calendar, Proton Drive, and Proton Pass. These tools empower individuals and organizations to take control of their personal data, break away from Big Tech’s invasive practices, and defeat censorship. Our work impacts hundreds of millions of lives, from activists on the front lines defending freedom to leaders in governments protecting sensitive information. In some cases, Proton’s services have even been instrumental in saving lives by enabling secure and private communications in high-risk situations.\nProton is a profitable company that does not rely upon VC funding, supporting over 100 million user accounts with a growing team of over 500 people from over 50 different countries, from the world's top companies and universities. We value intelligence, learning potential, and ambition in our hiring process. Adaptability is key as we navigate uncharted territories and redefine how business is conducted online.\nHiring at Proton is highly selective, with less than 1% of candidates hired. We believe smaller teams of exceptional talent will always prevail over larger teams with lower talent density. You will have the opportunity work with many of the world's top minds in their fields, ranging from former international math and science olympiad winners to chess champions.\nWe have a global mindset and big ambitions but remain a start-up at heart. We value empowerment and flexibility and keep our structure flat to keep moving fast and avoid unnecessary politics. Tired of blending into the crowd? Join us and do work you can truly be proud of. Check our open-source projects <a class="Lexical__link" href="https://proton.me/community/open-source">here</a>!\nThe Team\nThe Security Machine Learning Engineer will play a key role in transforming our Security Operations Center (SOC) from reactive to proactive by integrating advanced machine learning and data-driven approaches into our detection and response workflows.\nThis role bridges traditional cybersecurity operations and modern ML-driven analytics, enabling our team to automatically identify emerging threats, anomalous behaviour, and new attack patterns at scale. As a secondary focus, the role could also leverage LLMs and AI engineering to automate analyst workflows and reduce operational toil.\nThe engineer will sit directly within the security team, ensuring that the solutions built are operationally relevant, and aligned with our security priorities, while also working closely with the internal Machine Learning team (MSA) to leverage their expertise and best practices.\nWhat you will do:\n<ul>\n<li>\nML-Driven Detection & Automation\n<ul class="Lexical__ul Lexical__ul--depth-1">\n<li class="Lexical__listItem" value="1">Design, develop, and deploy machine learning models to enhance security detection, anomaly identification, and incident response.</li>\n<li class="Lexical__listItem" value="2">Integrate ML outputs into the SOC workflow to enable smarter and faster triage.</li>\n<li class="Lexical__listItem" value="3">Continuously evaluate and tune models to reduce false positives and improve detection precision.</li>\n<li class="Lexical__listItem" value="4">Ensure model outputs are interpretable and actionable for SOC analysts.</li>\n</ul>\nData Engineering for Security\n<ul class="Lexical__ul Lexical__ul--depth-1">\n<li class="Lexical__listItem" value="1">Build and maintain data pipelines to collect, process, and transform security-relevant data (e.g., logs, network traffic, endpoint events) into ML-ready datasets.</li>\n<li class="Lexical__listItem" value="2">Collaborate with security engineering team to ensure scalable and secure data handling (eg. parsing, processing, storage).</li>\n</ul>\nAI Engineering & LLM-Powered Automation\n<ul class="Lexical__ul Lexical__ul--depth-1">\n<li class="Lexical__listItem" value="1">Explore and build LLM-powered tools to automate repetitive SOC tasks (e.g., alert triage, evidence gathering, incident summarisation, report generation).</li>\n<li class="Lexical__listItem" value="2">Apply appropriate guardrails and evaluation to ensure outputs are accurate, auditable, and safe to act on in operational contexts.</li>\n</ul>\nResearch & Innovation\n<ul class="Lexical__ul Lexical__ul--depth-1">\n<li class="Lexical__listItem" value="1">Stay current on advancements in security data science, adversarial ML, and automated threat detection.</li>\n<li class="Lexical__listItem" value="2">Prototype and test new ML and AI techniques (e.g., unsupervised anomaly detection, graph-based threat correlation).</li>\n<li class="Lexical__listItem" value="3">Contribute to improving detection content through statistical analysis and clustering.</li>\n</ul>\nOperations & Maintenance\n<ul class="Lexical__ul Lexical__ul--depth-1">\n<li class="Lexical__listItem" value="1">Deploy models into production securely and responsibly, ensuring reliability and scalability.</li>\n<li class="Lexical__listItem" value="2">Implement monitoring, alerting, and retraining mechanisms for deployed ML models.</li>\n<li class="Lexical__listItem" value="3">Document methodologies and performance metrics for auditability and knowledge sharing.</li>\n</ul>\n</li>\n</ul>\nWhat we are looking for:\n<ul>\n<li>\nRequired\n<ul class="Lexical__ul Lexical__ul--depth-1">\n<li class="Lexical__listItem" value="1">Proven experience in machine learning engineering or data science, ideally in a cybersecurity or operations context.</li>\n<li class="Lexical__listItem" value="2">Proficiency in Python, with strong knowledge of ML frameworks.</li>\n<li class="Lexical__listItem" value="3">Experience with data manipulation and analysis using Pandas, NumPy or similar tools.</li>\n<li class="Lexical__listItem" value="4">Familiarity with security data sources (e.g., SIEM logs, EDR telemetry, network flow, authentication logs).</li>\n<li class="Lexical__listItem" value="5">Solid understanding of ML lifecycle: data preparation, model training, evaluation, deployment, and monitoring.</li>\n<li class="Lexical__listItem" value="6">Experience with data pipelines and storage technologies (e.g., Airflow, Kafka, Redis, Elasticsearch, Clickhouse, etc.).</li>\n<li class="Lexical__listItem" value="7">Ability to work independently and collaborate effectively with both ML and security specialists.</li>\n</ul>\nPreferred\n<ul class="Lexical__ul Lexical__ul--depth-1">\n<li class="Lexical__listItem" value="1">Prior experience in threat detection, SOC operations, or security automation.</li>\n<li class="Lexical__listItem" value="2">Knowledge of adversarial ML, graph analytics, or behavioral modeling in security contexts.</li>\n<li class="Lexical__listItem" value="3">Experience integrating ML models into SIEM pipelines or automated detection frameworks.</li>\n<li class="Lexical__listItem" value="4">Exposure to LLMs and AI engineering (e.g., prompt engineering, RAG, agent design), and awareness of LLM-specific risks like prompt injection and data leakage.</li>\n</ul>\nSuccess in This Role\n<ul class="Lexical__ul Lexical__ul--depth-1">\n<li class="Lexical__listItem" value="1">SOC analysts leverage ML-powered detections to identify threats faster.</li>\n<li class="Lexical__listItem" value="2">Reduction in alert fatigue and false positives through adaptive and data-driven models.</li>\n<li class="Lexical__listItem" value="3">Strong collaboration established between the security and MSA ML teams, sharing expertise and best practices.</li>\n<li class="Lexical__listItem" value="4">Security data becomes more accessible, structured, and usable for analytical and predictive use cases.</li>\n<li class="Lexical__listItem" value="5">New, intelligent detections, enrichment, and incident response automations become part of the SOC’s standard toolkit.</li>\n</ul>\n</li>\n</ul>\nEven if you don’t meet all the requirements listed above, but feel you could still be a great fit, please still apply.\nWhat We Offer:\n<ul class="Lexical__ul Lexical__ul--depth-1">\n<li class="Lexical__listItem" value="1">Work that Matters: millions of people trust Proton with their privacy. We answer only to our users — not advertisers, not investors with conflicting agendas, not governments. The work you do here is real, and the impact is measurable. (read more about our impact <a class="Lexical__link" href="https://proton.me/about/impact">here</a>)</li>\n<li class="Lexical__listItem" value="2">Technology: you'll get the right hardware and the right software you need to do your best work.</li>\n<li class="Lexical__listItem" value="3">Learning & Development: we invest in your growth because sharp people make us better. Proton is one of the fastest ways to accelerate your career because you'll be thrown into real challenges, with real ownership, from day one.</li>\n<li class="Lexical__listItem" value="4">Employee Benefits: your wellbeing isn't an afterthought. We offer strong health coverage, solid retirement options, generous leave, and wellness support so you can bring your best self to work every day</li>\n<li class="Lexical__listItem" value="5">Stock Options: at Proton, we all have the opportunity to be owners of the company. From day one, you have a real stake in what we're building. When Proton wins, you win.</li>\n<li class="Lexical__listItem" value="6">In-Person Collaboration: Amazing things happen when passionate, smart, and purposeful people get together in the same room. With offices across Geneva, Zürich, Barcelona, London and more, you'll spend most of your time collaborating face-to-face with people who genuinely care about what they're building</li>\n<li class="Lexical__listItem" value="7">Food: Lunch and snacks are on us every day in our offices so you can focus on the work and not on what's for lunch.</li>\n<li class="Lexical__listItem" value="8">Transport: getting to the office shouldn't cost you. We cover public transport, bike allowances, or parking, whichever works for you.</li>\n<li class="Lexical__listItem" value="9">Flexible Working: you own your schedule. Set hours that work for you and your team — because outcomes matter more than when the clock says you started.</li>\n</ul>\nOur Commitment to Diversity and Inclusion\nAt Proton, we believe diversity drives innovation and strengthens our mission to provide privacy as a default for all. We are committed to fostering an inclusive environment where all individuals, regardless of race, ethnicity, gender, age, sexual orientation, physical ability, or socio-economic background, feel valued and empowered. We strive to create equal opportunities, promote open dialogue, and support continuous learning to ensure every voice is heard and respected.\nIf you need any extra support or reasonable adjustments during the hiring process, please let your talent partner know.\nCandidate Privacy Notice\nWhen you apply for a position, refer a candidate, or are considered for a role at Proton Technologies AG (Proton, we, us, or our), your information is stored in Greenhouse, in accordance with their <a class="Lexical__link" href="https://www.greenhouse.com/privacy-policy">Service Privacy Policy.</a> This information is used to evaluate your suitability for the posted position. We also retain this information for consideration for future roles that you may apply for or that we believe may align with your background and skills.\nIf we no longer have a legitimate business need to process your information, we will either delete or anonymize it. Should you have any inquiries about how we use or manage your information, or if you wish to access, correct, or delete your data, please contact our privacy team at careers@proton.ch.\nProton does not accept unsolicited resumes from any sources other than directly from candidates. We will not pay a fee for any placement resulting from an unsolicited offer, even if the candidate is subsequently hired by Proton.\nTo learn more about our privacy policy, please visit <a class="Lexical__link" href="https://boards.eu.greenhouse.io/protongdpr">our privacy policy page</a>.\n \nCompensation range Paris:  46.000 - 74.000 gross annually* Other locations: Compensation will be discussed during the interview process *Final compensation will be determined based on the candidate's qualifications, skills, and previous experience\n \n#LI-ONSITE","Proton is hiring a Senior Machine Learning Engineer to transform its Security Operations Center with ML-driven threat detection, anomaly analysis, and incident-response automation. The role also explores LLM-powered tools that make SOC workflows faster, safer, and less repetitive.","This engineer will design, deploy, evaluate, and monitor machine learning models for security detection, anomaly identification, and incident response. The role includes building pipelines that transform logs, network traffic, endpoint events, and authentication data into ML-ready datasets. They will also prototype LLM-powered SOC automation for alert triage, evidence gathering, incident summarization, and report generation, with appropriate guardrails and evaluation. The position requires Python, ML frameworks, data analysis tools, security data familiarity, and experience across the ML lifecycle, with preferred experience in SOC operations, threat detection, adversarial ML, graph analytics, or LLM engineering. The engineer will work within Proton’s security team while collaborating closely with its internal Machine Learning team.",[33],"2026-08-17T14:47:46+00:00","2026-09-04T20:37:12.565552+00:00","2026-09-04T22:42:17.307212+00:00","Proton","proton","https://wzishhuxscpbsvxosipt.supabase.co/storage/v1/object/public/company_logos/companies/1e31c665-d9a0-4177-8894-1194b8b9b2fa.png",[54],[57,345],"Security",[214,347,348,349,350,351,129,352],"Apache Kafka","ClickHouse","Elasticsearch","NumPy","Pandas","Redis",[354],{"_17":54,"_62":63,"_64":65,"_66":67,"_68":69,"_70":71},[356,357,358,359,360,361,362,363,364,365,366],"Proven experience in machine learning engineering or data science, ideally in a cybersecurity or operations context.","Proficiency in Python and strong knowledge of machine learning frameworks.","Experience manipulating and analyzing data with Pandas, NumPy, or similar tools.","Familiarity with security data sources including SIEM logs, EDR telemetry, network flow, and authentication logs.","Understanding of the full machine learning lifecycle, including data preparation, training, evaluation, deployment, and monitoring.","Experience with data pipelines and storage technologies such as Airflow, Kafka, Redis, Elasticsearch, or ClickHouse.","Prior experience in threat detection, SOC operations, or security automation is preferred.","Knowledge of adversarial ML, graph analytics, or behavioral modeling in security contexts is preferred.","Experience integrating ML models into SIEM pipelines or automated detection frameworks is preferred.","Exposure to LLMs and AI engineering, including prompt engineering, RAG, and agent design, plus awareness of prompt injection and data leakage risks, is preferred.","Ability to work independently and collaborate with machine learning and security specialists.",[368,369,370,371,372,373],"Design, develop, deploy, evaluate, and tune machine learning models for security detection, anomaly identification, and incident response.","Integrate ML outputs into SOC workflows to improve triage speed, detection precision, interpretability, and analyst actionability.","Build and maintain pipelines that collect, process, transform, and securely store security-relevant data such as logs, network traffic, endpoint events, and authentication records.","Develop LLM-powered tools for alert triage, evidence gathering, incident summarization, and report generation with operationally appropriate guardrails.","Research and prototype unsupervised anomaly detection, graph-based threat correlation, adversarial ML, clustering, and other automated threat-detection techniques.","Deploy models securely into production and implement monitoring, alerting, retraining, documentation, and performance tracking.",[375,376,377,378,379,380,381,382,383],"Hardware and software needed to do the job effectively.","Learning and development investment with substantial ownership from the start.","Health coverage, retirement options, generous leave, and wellness support.","Stock options and company ownership opportunities.","Primarily in-person collaboration at Proton offices, including Geneva, Zürich, Barcelona, and London.","Free lunch and snacks in offices.","Public transport, bike allowance, or parking support.","Flexible working hours and schedule.","Paris compensation is listed as €46,000–€74,000 gross annually; compensation for other locations is discussed during interviews.","https://protonvpn.com",{"_17":386,"_19":387,"_21":388,"_23":389,"_25":390,"_27":391,"_29":-5,"_30":-5,"_31":392,"_34":393,"_36":394,"_38":395,"_39":-5,"_40":396,"_42":397,"_44":398,"_46":47,"_48":7,"_49":170,"_51":-5,"_52":399,"_55":400,"_58":401,"_59":408,"_72":410,"_74":419,"_76":426,"_78":-5,"_79":-5,"_80":-5,"_81":-5,"_82":83,"_84":-5,"_85":7,"_86":438},"3bd9451d-821e-4249-b268-000e01468625","Senior AI Engineer - Patient Team (x/f/m)","https://job-boards.greenhouse.io/doctolib/jobs/7792273003","<h2>Join our mission, join Doctolib! </h2>\nWe are looking for a Senior AI Engineer to join the Patient team in Paris. \nThe Patient domain sits at the heart of Doctolib's mission: ensuring everyone has better access to the care they need, receives better care from health professionals, and can actively prevent health problems to improve their wellbeing.\nYou’ll design the search and recommendation engines behind our health companion, helping 100M patients across Europe instantly navigate to the exact care they need while delivering trusted, curated insights at every step. The retrieval and recommendation architecture you own will directly shape how relevant, fast, and trustworthy that experience is for every one of them.\n Your responsibilities include but are not limited to:\n<ul>\n<li>Design and build the production search & recommendation architecture: full retrieval, ranking, reranking pipeline with standard and off-the-shelf components (vector search, semantic retrieval, LLM/managed rerankers).</li>\n<li>Establish strong baselines first (prompts, RAG, model selection) before reaching for custom ML.</li>\n<li>Build evaluation and observability into every stage, with offline and online evaluation.</li>\n<li>Set up the data/event feedback loops that drive iteration and feed deeper ML later.</li>\n<li>Improve search relevance and ranking on Patient facing products , raising result quality</li>\n<li>Own production quality: latency reliability, monitoring, and maintainability.</li>\n<li>Partner with ML Engineers and collaborate closely with PMs and SWEs to define, build, and ship AI-powered features that deliver measurable value to users and the business. </li>\n</ul>\n<h2> Who you are</h2>\nBefore you read on:  if you don't have the exact profile described below, but you feel this job description matches your skill set, we still encourage you to apply.\nYou could be our next team mate if you have:\n<ol>\n<li>Production deployment: ability to ship algorithms to production (ECS-based service on AWS)</li>\n<li>Strong analytical mindset: result-oriented,  patient-first approach</li>\n<li>Significant experience as a Software or/and AI engineer shipping search or recommendation systems to production. </li>\n<li>Hands-on experience building end-to-end retrieval: ranking,  reranking pipelines and familiar with nDCG, MAP, Recall@k, MRR</li>\n<li>AI-engineering proficiency: turning foundation models and off-the-shelf components into production systems: embeddings & vector search, semantic retrieval, RAG, LLM-based or managed rerankers (e.g. Vertex AI). You can succeed without training a model from scratch</li>\n<li>Architecture-first approach: you build the system, baselines, evals, and feedback loops with standard tooling before reaching for custom ML, and know when to partner with ML Engineers to break a ceiling</li>\n<li>Evaluation & observability built into every stage (retrieval, ranker, reranker) — offline and online eval, A/B testing, position-bias handling, monitoring</li>\n<li>Production deployment — ability to ship reliable, low-latency services to production (hundreds-of-ms SLAs), with care for data quality and long-term maintainability</li>\n</ol>\n \nNow, it would be fantastic if you:\n<ul>\n<li>Experience at a B2C marketplace (e-commerce, hospitality, travel)</li>\n<li>Additional ML methodologies: pattern mining, recommendation systems, experimentation, or causal inference</li>\n<li>Have experience with search engines or information retrieval concepts</li>\n<li>Have exposure to learning-to-rank or feature engineering (for breaking ceilings later, alongside ML Engineers)</li>\n<li>Have experience in a healthcare or other regulated domain (GDPR / HDS) </li>\n</ul>\n<h2 class="s-pb-2 s-pt-4 s-heading-xl s-text-foreground dark:s-text-foreground-night">Life at Doctolib Tech</h2>\n<ul class="s-list-disc s-pb-2 s-pl-6 s-text-foreground dark:s-text-foreground-night s-text-base s-leading-7">\n<li class="s-break-words s-text-foreground dark:s-text-foreground-night s-text-base s-leading-7">Our solutions are built on a single, fully cloud-native platform that supports web and mobile app interfaces, multiple languages, and is adapted to country and healthcare specialty requirements.</li>\n<li class="s-break-words s-text-foreground dark:s-text-foreground-night s-text-base s-leading-7">Our stack is composed of Rails, TypeScript, Java, Python, Kotlin, Swift, and React Native.</li>\n<li class="s-break-words s-text-foreground dark:s-text-foreground-night s-text-base s-leading-7">We leverage AI ethically across our products to empower patients and health professionals. Discover our AI vision <a class="s-break-all s-font-semibold s-transition-all s-duration-200 s-ease-in-out hover:s-underline s-text-highlight dark:s-text-highlight-night hover:s-text-highlight-400 dark:hover:s-text-highlight-400-night active:s-text-highlight-dark dark:active:s-text-highlight-dark-night" href="https://careers.doctolib.com/ai-careers/" target="_blank">here</a>.</li>\n<li class="s-break-words s-text-foreground dark:s-text-foreground-night s-text-base s-leading-7">We also invest in open, applied AI research. For example, our DoctoBERT project introduces open-source medical language models trained for French clinical text, with applications in named entity recognition, classification, and retrieval.<a href="https://huggingface.co/blog/hugging-science/doctobert-cookbook#introduction" target="_blank"> Read the DoctoBERT practical guide</a> to learn more about how we bring deep AI research into practical healthcare applications.</li>\n</ul>\n<div class="s-whitespace-pre-wrap s-break-words s-font-normal first:s-pt-0 last:s-pb-0 s-py-1 @md:s-py-2 @md:s-leading-7 s-text-base s-leading-7 s-text-foreground dark:s-text-foreground-night">Want to learn more about our tech culture and environment? Visit the <a class="s-break-all s-font-semibold s-transition-all s-duration-200 s-ease-in-out hover:s-underline s-text-highlight dark:s-text-highlight-night hover:s-text-highlight-400 dark:hover:s-text-highlight-400-night active:s-text-highlight-dark dark:active:s-text-highlight-dark-night" href="https://careers.doctolib.com/tech-doctolib/" target="_blank">Doctolib Tech site</a>. </div>\n<h2>What we offer</h2>\n<ul>\n<li>Free comprehensive health insurance (basic package) for you and your children</li>\n<li>25 days of paid vacation per year, plus up to 14 days of RTT</li>\n<li>Free mental health and coaching services through our partner Moka.care</li>\n<li>Work from abroad for up to 10 days per year thanks to our flexibility days policy</li>\n<li>Lunch vouchers (Swile card) worth €8.50 per working day, with €4.50 covered by Doctolib</li>\n<li>A subsidy from the work council to refund part of the membership to a sport club or a creative class</li>\n<li>50% reimbursement of your public transport subscription</li>\n<li>Enrollment in Doctolib's long-term employee value sharing plan called DoctoGrowth</li>\n<li>ParentCare Program: Enjoy full salary coverage (100%) during your first month of birth leave, and 75% during the second, covered by Doctolib</li>\n<li>For caregivers and workers with disabilities, a package including an adaptation of the remote policy, extra days off for medical reasons, and psychological support</li>\n</ul>\n \n<h2>The interview process </h2>\n<ul>\n<li>A first interview with a member of the Talent Acquisition team</li>\n<li>A coding interview</li>\n<li>An agentics system design </li>\n<li>A final interview </li>\n<li>Reference check </li>\n</ul>\n \n<h2>Job details</h2>\n<ul>\n<li>Permanent position</li>\n<li>Full Time </li>\n<li>Workplace: Levallois</li>\n<li>Start date : As soon as possible</li>\n</ul>\n \n \nAt Doctolib, we are committed to improving access to healthcare for everyone. This translates into our recruitment process. We evaluate candidates based solely on qualifications and motivation, without any form of discrimination.\nThe more diverse ideas are heard, the more our product will truly improve healthcare for all. You are welcome to apply to Doctolib, regardless of your gender, religion, age, sexual orientation, ethnicity, disability.\nTo ensure equal opportunities, we invite you to exclude personal information (e.g. pictures, age) from your applications. If you require any accommodation, please let us know for support during the hiring process. \nJoin us in building the healthcare we all dream of!\nAll information provided is processed by Doctolib for application management. For data processing details, click here: <a href="https://doctolib.legal/B2C-Privacy-Policy-Career-DE">Germany</a> l <a href="https://doctolib.legal/B2C-Privacy-Policy-Career-FR">France</a> l <a href="https://doctolib.legal/B2C-Privacy-Policy-Career-IT">Italy</a> l <a href="https://doctolib.legal/B2C-Privacy-Policy-Career-COM">Netherlands</a>. Please contact hr.dataprivacy(at)doctolib.com to exercise your rights.","Join Doctolib’s Patient team as a Senior AI Engineer building the search and recommendation systems that help millions of patients find relevant healthcare and trusted insights. You’ll own production retrieval, ranking, reranking, evaluation, and feedback-loop architecture.","This role designs and builds production search and recommendation systems, including retrieval, ranking, reranking, vector search, semantic retrieval, and LLM-based components. You will establish reliable baselines, implement offline and online evaluation, and create observability and feedback loops to improve relevance over time. The position requires significant experience shipping search or recommendation systems and reliable, low-latency services to production on AWS-based infrastructure. You will collaborate with ML Engineers, product managers, and software engineers to deliver measurable AI-powered features for patient-facing products. Experience with B2C marketplaces, learning-to-rank, experimentation, healthcare, or regulated environments is preferred.",[33],"2026-08-05T13:37:56+00:00","2026-07-03T18:32:41.509834+00:00","2026-09-04T22:36:16.542946+00:00","Doctolib","doctolib","https://wzishhuxscpbsvxosipt.supabase.co/storage/v1/object/public/company_logos/companies/890c1608-d49f-4ab3-a3ac-c6bf6f6fe421.png",[54],[57],[124,402,403,129,404,405,406,407],"Java","Kotlin","React Native","Ruby on Rails","Swift","TypeScript",[409],{"_17":54,"_62":63,"_64":65,"_66":67,"_68":69,"_70":71},[411,412,413,414,415,416,417,418],"Significant experience as a Software Engineer and/or AI Engineer shipping search or recommendation systems to production.","Ability to deploy algorithms to production using ECS-based services on AWS.","Hands-on experience building end-to-end retrieval, ranking, and reranking pipelines.","Familiarity with information-retrieval metrics including nDCG, MAP, Recall@k, and MRR.","Proficiency turning foundation models and off-the-shelf components into production systems, including embeddings, vector search, semantic retrieval, RAG, and LLM-based or managed rerankers.","Experience building offline and online evaluations, A/B tests, position-bias handling, monitoring, and data-quality processes.","Ability to build reliable, maintainable services with latency in the hundreds-of-milliseconds range.","Experience in a B2C marketplace, search engines, learning-to-rank, feature engineering, experimentation, causal inference, recommendation systems, pattern mining, healthcare, or regulated domains such as GDPR/HDS is preferred.",[420,421,422,423,424,425],"Design and build production search and recommendation architecture across retrieval, ranking, and reranking pipelines.","Use vector search, semantic retrieval, RAG, foundation models, and LLM or managed rerankers to improve patient-facing search relevance.","Establish baselines, evaluation systems, observability, monitoring, and offline and online measurement at every pipeline stage.","Create data and event feedback loops that support iteration and future machine-learning improvements.","Own production quality, including latency, reliability, maintainability, and low-latency service performance.","Partner with ML Engineers, product managers, and software engineers to define, build, and ship AI-powered features.",[427,428,429,430,431,432,433,434,435,436,437],"Permanent, full-time position in Levallois with an ASAP start date.","Free comprehensive health insurance for employees and children.","25 days of paid vacation plus up to 14 days of RTT.","Free mental health and coaching services through Moka.care.","Ability to work from abroad for up to 10 days per year.","€8.50 daily lunch vouchers through Swile, with €4.50 covered by Doctolib.","Work-council subsidy for sport-club or creative-class memberships.","50% reimbursement of public transport subscriptions.","Enrollment in the DoctoGrowth long-term employee value-sharing plan.","ParentCare birth-leave salary coverage of 100% during the first month and 75% during the second month.","Additional support for caregivers and workers with disabilities, including remote-policy adaptations, medical leave, and psychological support.","http://www.doctolib.fr","actionData","errors"]

Senior AI & ML engineer

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