ml engineer for biomedical foundation models

Описание:

Bohringer Ingelheim’s AI Accelerator builds production-quality AI capabilities to improve understanding of disease biology and increase the probability of successful medicines. The AI Systems team designs, builds and deploys multimodal foundation models across biomedical data to enhance and accelerate portfolio decision-making.

Задачи:

• Bring ML engineering expertise into architectural design, ensuring foundation models are efficient and scalable;
• Implement biomedical foundation model components, including training code, data loaders, tokenisers, inference logic and fine‑tuning interfaces;
• Translate validated research prototypes into robust, production-quality model artefacts and contribute to benchmarking and performance evaluation;
• Optimise validated models and inference pipelines using quantisation, distillation and pruning to meet production efficiency and latency requirements;
• Write clean, well‑tested and well‑documented code and uphold engineering standards across the team;
• Lead model handovers to MLOps engineers with documentation covering capabilities, known limitations, failure modes and retraining criteria;
• Stay current with advances in ML engineering, distributed training and biomedical AI tooling.

Требования:

• Hold a postgraduate degree in Machine Learning, Computer Science, Computational Biology or a related technical field;
• Have a PhD or an MSc with equivalent industry experience;
• Have hands‑on experience with deep learning and foundation model implementations, including transformers, pre‑training and fine‑tuning, ideally at scale;
• Have experience delivering production‑quality model artefacts and moving research prototypes to reliable deployment;
• Have experience partnering closely with researchers throughout implementation;
• Be proficient in Python and deep learning frameworks such as PyTorch or JAX;
• Have strong software engineering fundamentals, including clean, testable, well‑documented and maintainable code, version control and code reviews;
• Have experience with distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP or Ray Train;
• Have experience optimising models for inference, including quantisation, distillation and pruning;
• Nice to have: experience with biomedical data modalities such as genomics, multi‑omics, clinical or imaging data in an ML context;
• Publications or contributions to open‑source ML projects or tooling.

Условия:

• Hybrid role with approximately 3 days a week in the office;
• Boehringer Ingelheim has been recognised as a Top Employer in the UK;
• Access to health and wellbeing programs and groups;
• The company invests in global accessibility to healthcare.

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