ml engineer in biomedical AI in London

Boehringer Ingelheim is a biopharmaceutical company active in human and animal health. It develops innovative therapies in areas of high unmet medical need and invests in research and development.

Responsibilities:

• Contribute engineering perspectives to architectural design discussions to support efficient training and inference, scalability, and production readiness.
• Implement biomedical foundation model components, including training code, data loaders, tokenisers, inference logic, and fine-tuning interfaces, under the guidance of senior ML engineers.
• Collaborate with AI scientists to translate validated research prototypes into robust, production-quality model artefacts and contribute to benchmarking and performance evaluation.
• Optimise validated models and inference pipelines using techniques such as quantisation, distillation, and pruning to meet production efficiency and latency requirements without compromising performance.
• Write clean, well-tested, and well-documented code in line with the team's engineering standards.
• Support model handovers to MLOps engineers and document capabilities, known limitations, failure modes, and retraining criteria.
• Stay current with advances in ML engineering, distributed training, and biomedical AI tooling.

Requirements:

• Postgraduate degree in Machine Learning, Computer Science, Computational Biology, or a related technical field.
• Hands-on experience with deep learning and foundation model implementations, such as transformers, pre-training, and fine-tuning.
• Some experience contributing to production-quality model artefacts and a growing understanding of moving research prototypes to reliable deployment.
• Experience collaborating with researchers during implementation.
• Proficiency in Python and deep learning frameworks such as PyTorch.
• Strong software engineering fundamentals, including clean, testable, documented, and maintainable code, version control, and code reviews.
• Working knowledge of distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP, or Ray Train.
• Exposure to inference optimisation techniques such as quantisation, distillation, and pruning.

Desirable:

• PhD or equivalent industry experience with an MSc.
• Experience with biomedical data modalities such as genomics, multi-omics, clinical or imaging data in an ML context.
• Publications, contributions to open-source ML projects or tooling.

Conditions:

• This is a hybrid role with 3 days a week in the office.
• Recognised as a Top Employer in the UK.
• Access to health and wellbeing programmes and groups.