2 months ago
London, United KingdomMid Level
Responsibilities
- Own and advance a predictive modeling platform that scales across problem types and tenants.
- Design, implement, and iterate models for vulnerabilities in housing, health, and other social domains.
- Track ML and frontier-model developments and run structured experiments to bring promising techniques into production.
- Build training datasets, evaluation pipelines, and model infrastructure for natural language and predictive analytics.
- Ensure responsible AI deployment by incorporating ethical and regulatory considerations throughout development.
- Implement and deploy production APIs and containerized systems, including asynchronous services for embedding and compute-intensive workloads.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience.
- At least 3 years of professional experience as an ML Engineer or in a related role.
- Strong Python programming skills and production experience.
- Experience building and maintaining data or ML pipelines with orchestration tools such as Dagster, Airflow, or Prefect.
- Hands-on experience with PyTorch, scikit-learn, XGBoost, LightGBM, or similar ML libraries and frameworks.
- Experience implementing internal APIs, deploying containerized systems with Kubernetes, and building production-ready asynchronous APIs.
- Preferred qualifications include a PhD and strong publications in text analytics, representation learning, or applied predictive modeling.
- Preferred experience includes productionizing LLMs, configuring vector databases, developing RAG pipelines, using Hugging Face Transformers or OpenAI APIs, and building agentic systems with LangChain, AutoGen, or PydanticAI.
- Evidence of open-source development, public hackathons, or sharable coding samples is advantageous.
- Deep expertise in embedding-based architectures, including bi-encoders and cross-encoders, is advantageous.
- Proficiency in high-performance Python data and model pipelines and familiarity with the Azure ecosystem are advantageous.
Benefits
- Hybrid work with an expectation of being in the office 1–2 days per week.
- Competitive salary reviewed annually.
- Flexible hours around life commitments.
- Training and development opportunities.
- 25 days of annual leave plus bank holidays.
- Company pension and private medical insurance.
- Enhanced parental leave, cycle-to-work scheme, flu vaccinations, and eye-test and glasses support.
- Employee Assistance Programme with mental health support, remote GP access, counseling or therapy, physiotherapy, and medical second opinions.
Tech Stack
Apache AirflowAzureFastAPIHugging Face TransformersKubernetesLightGBMPythonPyTorchscikit-learnXGBoost
Categories
