Senior ML Engineer

            THE AI ACCELERATOR

The AI Accelerator is a brand-new, London-based hub, sitting within Computational Innovation (CI), which is a global organisation comprising computational biology, human genetics, data excellence and AI expertise.   The purpose of CI’s AI Accelerator is to provision production-quality, versatile, foundational biomedical AI capabilities that can be adapted and deployed to improve and accelerate portfolio decision-making and increase the probability of success, by furthering understanding of the biology driving patient outcomes and identifying mechanisms involved in disease.   A core component of the AI department is AI Systems, a team focused on designing, building and deploying versatile biomedical foundation models that, through adaptation, can enhance human understanding of disease biology and help identify potential targets, biomarkers and patient segments for further research.  AI Systems will exploit neural-based methods to integrate data and impute and infer across the biomedical landscape. It could be electronic health records and medical imaging to support patient segmentation. It could be ‘omics data to understand gene regulation and identify novel therapeutic concepts; it could be predicting transcriptional changes for a given disease-causing variant. 

THE POSITION We are seeking a Senior ML Engineer to join the Accelerator’s AI Systems team (@computationalinnovation) and deliver next generation, foundational AI capabilities to support discovery and development of innovative medicines.  You will be an experienced independent ML Engineer within AI Systems, responsible for delivering production model components and capabilities to a high engineering standard. You work under the implementation direction set by the Senior Staff ML Engineer, in close partnership with AI Scientists whose validated research prototypes and architectural designs you bring to production.   You will engage early in architectural discussions to contribute production engineering perspectives on training efficiency, scalability and production-readiness, iterating together with AI scientists on design decisions and maintaining active dialogue throughout.   Your work is primarily hands-on implementation. You will write training code, build biomedical-specific data loaders and tokenisers, implement model components and write model-specific inference logic and fine-tuning code to a high engineering standard. You are expected to operate independently on defined implementation workstreams, growing your ability to handle increasingly complex technical challenges and contributing more actively to technical decisions over time.