20 days ago
London, United KingdomMid Level
Responsibilities
- Own and evolve a predictive modeling platform that scales across problem types and tenants.
- Design, implement, and iterate embedding-based sequence encoders, temporal survival models, and gradient-boosted decision trees.
- Track ML and frontier-model developments and run structured experiments to bring promising techniques into production safely.
- Build evaluation pipelines, training datasets, and model infrastructure for continuous improvement of natural-language and predictive analytics.
- Implement and deploy production APIs and containerized systems, including asynchronous services for embedding and compute-intensive workloads.
- Embed ethical and regulatory considerations throughout responsible AI development and deployment.
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 Dagster, Airflow, Prefect, or a similar orchestration tool.
- Hands-on experience with PyTorch, scikit-learn, and gradient-boosting libraries such as XGBoost or LightGBM.
- Experience implementing internal APIs, deploying containerized systems to production through Kubernetes, and building production-ready asynchronous APIs.
- Preferred qualifications include a PhD and publication record in text analytics, representation learning, or applied predictive modeling.
- Preferred experience includes productionizing LLMs, vector databases, RAG pipelines, Hugging Face Transformers, OpenAI APIs, LangChain, AutoGen, and PydanticAI.
- Evidence of open-source development, public hackathons, or sharable coding samples is advantageous.
- Deep expertise in embedding-based architectures such as bi-encoders and cross-encoders is advantageous.
- Proficiency in Python for high-performance data and model pipelines, with software engineering discipline around testing, versioning, and CI/CD.
- Familiarity with Azure services including Azure Kubernetes Service, Azure Batch, Azure AI Foundry, Azure Machine Learning, Azure Blob Storage, and Azure Key Vault is advantageous.
Benefits
- Hybrid work arrangement 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 policies.
- Cycle to work scheme, flu vaccinations, eye tests, and contributions toward glasses for VDU use.
- Employee Assistance Programme with mental health and wellbeing support, remote GP access, counselling and therapy, physiotherapy, and medical second opinions.
Tech Stack
Apache AirflowAzureFastAPIHugging Face TransformersKubernetesLightGBMPythonPyTorchscikit-learnXGBoost
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