Senior ML Engineer

Most diseases are still poorly understood at a biological level. Despite decades of research, the causal mechanisms driving many conditions remain unclear, limiting our ability to identify the right targets, design the right interventions and bring the right medicines to patients. The AI Accelerator exists to change that. Based in London and sitting within Computational Innovation, a global organisation spanning computational biology, human genetics, data excellence and AI, the Accelerator’s mission is to build production-quality AI capabilities that deepen our understanding of disease biology and increase probability of success.

We do this by applying neural-based methods across the biomedical data landscape to integrate heterogeneous, multimodal data sources, infer biological relationships and embed causal thinking into what we build. The goal is not just to predict but to explain and understand why disease occurs. It could be electronic health records and medical imaging to support patient segmentation. It could be ‘omics data to identify novel therapeutic targets. It could be predicting transcriptional change for a given disease-causing variant. It could be simulating the effect of modulating a target of interest.

A core component of the AI Accelerator is AI Systems, a team focused on designing, building and deploying multimodal foundation models across the vast biomedical data landscape that will be used within Computational Innovation to enhance and accelerate portfolio decision-making.

Key Responsibilities

• Bring production engineering expertise into architectural design from the start, ensuring foundation models are built to be efficient, scalable and deployment-ready.
• Implement biomedical foundation model components such as training code, data loaders, tokenisers, inference logic and fine-tuning interfaces to a high engineering standard.
• Work closely with AI scientists to translate validated research prototypes into robust, production-quality model artefacts and contribute to benchmarking and performance evaluation.
• Write clean, well-tested, well-documented code and uphold engineering standards across the team.
• Lead model handovers to MLOps engineers, with thorough documentation covering capabilities, known limitations, failure modes and retraining criteria.
• Stay current with and bring back to the team, advances in ML engineering, distributed training and biomedical AI tooling.

Required Qualifications

• PhD in Machine Learning, Computer Science, Computational Biology or a related quantitative field.
• Solid hands-on experience with deep learning and foundation model implementations such as transformers, pre-training, fine-tuning, ideally at scale.
• Demonstrated experience delivering production-quality model artefacts, with a strong sense of what’s required to move from research prototypes to reliable deployment.
• Proficiency in Python and deep learning frameworks such as PyTorch or JAX.
• Strong software engineering fundamentals - writing clean, testable, well-documented and maintainable code, version control, code reviews.
• Experience with distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP or Ray Train.
• Experience working with biomedical data modalities such as genomics, multi-omics, clinical or imaging data in an ML context is advantageous.
• Experience working in close partnership with researchers throughout the implementation process.
• Publications/Contributions to open-source ML projects or tooling.

This is a hybrid role with approximately three days a week in the office.

Boehringer Ingelheim has been recognised as a Top Employer in the UK, demonstrating our commitment to building an exceptional workplace through strong people practices and supportive HR policies.