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 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.

Key Responsibilities

• Implement biomedical foundation model components from validated research prototypes in close collaboration with AI Scientists
• Implement model-specific inference logic, input/output interfaces and fine-tuning code
• Write clean, well-tested, well-documented code that meets the engineering standards set by the Senior Staff ML Engineer
• Validate model implementations against research prototypes
• Contribute to benchmarking runs and performance evaluation in collaboration with AI Scientists and stakeholder teams
• Stay current with advances in ML engineering best practices, 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
• Experience delivering production-quality model artefacts for downstream consumption
• Proficiency in Python and deep learning frameworks such as PyTorch or JAX)
• Strong understanding of software engineering practices - 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 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

Second round interviews will take place week commencing 22nd June.

This is a hybrid role with approximately 3 days a week in the office.

Why This Is A Great Place To Work

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