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 (@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.

We are looking for a Senior ML Engineer to join the AI Systems team and work at the frontier of biomedical AI. This is a hands-on engineering role with real stakes as the models you build will be used to make decisions about which indications to pursue, in which patient population and against which target.

You will work in close partnership with AI scientists, taking validated research prototypes and architectural designs and bringing them to production at a high engineering standard. You’ll engage early in architectural decisions, contributing engineering perspectives on training, efficiency, scalability and production-readiness, and iterating with AI scientists on design decisions.

This is a role for someone who takes pride in engineering craft, who writes clean, well-tested, well-documented code, who ensures that biology doesn’t lose its integrity when moving from research to production. Your engineering work will go far beyond the model card; it will connect directly to human health outcomes.

• Bring ML engineering expertise into architectural design from the start, ensuring foundation models are designed to be efficient and scalable
• 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
• Optimise validated models and inference pipelines, applying optimisation techniques such as quantisation, distillation and pruning to meet production efficiency and latency requirements without compromising model performance
• 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 advances in ML engineering, distributed training and biomedical AI tooling

• Postgraduate degree in Machine Learning, Computer Science, Computational Biology or a…