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 (@computationalinnovation), 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.