Machine Learning Engineer

2 days ago

Remote, United StatesSenior

H1B Sponsor

Base Salary

$156k - $229k/yr

Responsibilities

Partner with product, UX, and technical stakeholders to define business problems, requirements, scope, and measurable ML problem statements.
Design, implement, and maintain scalable, enterprise-grade ML solutions in production.
Build reproducible workflows for data preparation, training, evaluation, and inference using orchestration and MLOps tooling.
Implement monitoring and evaluation frameworks to improve data quality, model performance, latency, and cost through feedback loops.
Collaborate with Product, Data Science, Engineering, and Security teams to deliver resilient, scalable, and compliant ML services.
Own operational excellence, including SLAs, on-call support, incident response, customer feedback triage, and blameless post-mortems.
Drive engineering excellence through AI-assisted development, code reviews, automated testing, MLOps practices, knowledge sharing, and mentoring.

Strong foundation in machine learning and AI, including statistics, probability, and optimization, with the ability to apply them to real-world problems.
5+ years of experience building, deploying, and operating data and ML systems in production.
Proficiency in Python, Java, and SQL, with strong software engineering fundamentals in system design, testing, version control, and code reviews.
Hands-on experience with workflow orchestration, data pipelines, cloud data platforms, and storage systems.
Experience across the ML lifecycle and with MLOps, LLM or agent frameworks, and model evaluation or observability tools.
Working knowledge of Docker, Kubernetes, GitOps or CI/CD tools, and at least one of AWS, GCP, or Azure.
Understanding of data modeling, distributed computing, scalable systems, and streaming frameworks; GPU implementation experience is a plus.
Strong written and verbal communication skills and the ability to operate effectively in new domains and collaborative Agile environments.
Preferred qualifications include experience with recommendation systems, time-series modeling, representation learning, anomaly detection, causal inference, LLM fine-tuning, RAG, vector databases, open-source contributions, technical publications, relevant domain experience, and AI-assisted development tools.
An M.S. or Ph.D. in a relevant field is preferred.

Remote-first work arrangement with occasional in-person team, project, customer, or off-site meetings and occasional travel.
Competitive pay, paid sick time, paid personal time off, paid parental leave, healthcare insurance, retirement savings through a 401(k), wellness leave, and generous time off.
Potential eligibility for Twilio's equity plan and corporate bonus plan.
Applications are intended to be accepted until April 20, subject to change based on business needs.