The Position We are seeking a Senior Staff ML Engineer to join a Computational Innovation AI function (@computationalinnovation) that will deliver next generation foundational AI capabilities to support discovery and development of innovative medicines. A core component of the AI function 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. The Senior Staff ML Engineer is a technical leader in AI Systems who sets the standard for production-grade machine learning through deep hands-on expertise, pattern-setting and technical judgement. In this role, you will focus on solving the hardest engineering problems required to translate cutting-edge AI research into reliable, scalable production systems. Working in close partnership with AI Scientists, the Senior Staff ML Engineer defines implementation approaches, establishes reusable engineering patterns and personally tackles the most complex challenges in training, optimisation, deployment and fine-tuning of large-scale biomedical models. The successful candidate will be a senior technical authority within AI Systems and a member of the AI Enablement Leadership Team, influencing platform direction and infrastructure evolution through clear technical requirements and hands-on experience. This is a rare opportunity to shape how production machine learning is done for biomedical data at scale, while remaining deeply embedded in the most challenging and impactful technical work. Key Responsibilities - Personally tackle the most complex ML engineering challenges, including sophisticated multi-modal architectures, advanced training frameworks and critical-path production implementations - Establish production implementation patterns and standards for model code structure, configuration management, abstraction layers and distributed training approaches - Define and document reusable engineering patterns, templates and decision frameworks that multiply the effectiveness of Staff and Senior ML Engineers - Partner closely with AI Scientists to provide early architectural feedback, balancing biological fidelity, scalability and efficiency - Optimise the most demanding components of foundation models, including extreme-scale training, memory-efficient implementations and complex fusion mechanisms - Lead on the most challenging training, debugging and optimisation problems, particularly for large parameter models and long-sequence workloads - Build and influence development of sophisticated fine-tuning frameworks, inference systems and deployment patterns for production endpoints - Represent AI Systems ML engineering needs within the AI Enablement Leadership Team, influencing platform roadmap, infrastructure priorities and tooling evolution - Mentor Staff and Senior ML Engineers through technical leadership, raising engineering quality and establishing best practice across the team - Identify infrastructure gaps and work with AI Enablement to ensure production ML requirements are met at scale - Requirements - MSc or PhD (preferred) in Machine Learning, Computer Science, Mathematics, Physics, Computational Biology or equivalent experience - Extensive experience in software engineering and production ML systems, with deep hands-on expertise in translating research into production - Expert-level knowledge of modern ML frameworks (e.g. PyTorch or JAX) distributed training and federated learning approaches - Proven experience training and deploying large-scale models in production environments, preferably in the biomedical domain - Strong software engineering fundamentals, including code quality, testing and maintainability - Experience working with complex biomedical data modalities and translating biological constraints into robust implementations - Demonstrated technical leadership through pattern-setting, mentorship and cross-team influence without line management responsibility - Ability to articulate complex technical concepts and infrastructure requirements clearly to platform teams and senior stakeholders - Track record of building reusable components, improving research-to-production workflows and raising engineering standards All qualified applicants will receive consideration for employment without regard to a person's actual or perceived race, including natural hairstyles, hair texture and protective hairstyles; color; creed; religion; national origin; age; ancestry; citizenship status, marital status; gender, gender identity or expression; sexual orientation, mental, physical or intellectual disability, veteran status; pregnancy, childbirth or related medical condition; genetic information (including the refusal to submit to genetic testing) or any other class or characteristic protected by applicable law.