Staff AI/ML Engineer - Controllable Biology

At GSK, we have bold ambitions for patients, aiming to positively impact the health of 2.5 billion people by the end of the decade. Our R&D focuses on discovering and delivering vaccines and medicines, combining our understanding of the immune system with cutting-edge technology to transform people’s lives. GSK fosters a culture ambitious for patients, accountable for impact, and committed to doing the right thing, making sure that we focus our efforts on accelerating significant assets that meet patients’ needs and have the highest probability of success.

Our approach to R&D involves advanced applications of machine learning and AI to develop novel therapies for existing diseases and respond quickly to emerging or changing diseases with personalized drugs, driving better outcomes at reduced cost and with fewer side effects. The AI/ML Controllable Biology Team applies machine learning and AI methods to biological networks and sequence data from large-scale human genetic, functional genomic, and single-cell experiments. Models that control biological networks have the potential to be transformative in drug discovery, empowering us to find new life-saving medicines.

We are looking for a Staff AI/ML Engineer – Controllable Biology. Competitive candidates will have a track record of developing SOTA deep learning models to solve challenging real-world scientific problems. You should be an outstanding scientist with in-depth knowledge of modern machine learning. You can convert vaguely described biological and drug discovery challenges into well-defined machine learning problems. You can independently execute and deliver full AI/ML-driven solutions end to end: sourcing training data, designing and implementing SOTA machine learning models, defining metrics and benchmarks, and shipping stable, tested, performant code and services in an agile environment.

The AI/ML team is built on the principles of ownership, accountability, continuous development, and collaboration. We hire for the long term, and we're motivated to make this a great place to work. Our leaders will be committed to your career and development from day one.

Key Responsibilities:

• Convert complex biological questions into tractable mathematical problems that can be solved by modern computational methods.
• Be adept at decomposing large problems into quarterly, measurable results and consistently working towards delivering these through engineering sprints.
• Be a technical mentor in a multidisciplinary engineering team, including delegating tasks to junior colleagues and guiding them through delivery, while fostering an inclusive, supportive team culture.
• Be comfortable demoing early and often, balancing research and engineering velocity.
• Be a standard-bearer for machine learning, software engineering, code review and agentic development best practices within the organisation.
• Define technical strategy and roadmaps for AI/ML products, balancing scientific needs, safety, and operational reliability.
• Operate in a transparent way, communicating clearly and accurately to leadership and the broader organisation.
• Partner with stakeholders to ensure models are explainable, safe, and relevant.

Basic Qualifications:

• Master’s degree in a related field (e.g. computer science, mathematics or natural sciences).
• 7+ years’ experience with standard deep learning algorithms, model architectures, machine learning best practices, scalable training and deployment.
• 7+ years’ experience in software development, including code reviews, version control systems, CI/CD pipelines, software testing, technical documentation and Agile delivery methodologies.
• 5+ years’ experience in a technical lead or engineering manager role with direct reports, mentoring software engineers or machine learning practitioners.
• 7+ years’ experience with Python and PyTorch, or an equivalent machine learning framework.
• Experience working with biological sequence and network data, including genomics, transcriptomics, proteomics, gene regulatory networks or related biological datasets.
• Experience collaborating with stakeholders across multiple teams, functions and geographic regions.

Preferred Qualifications:

• PhD in a quantitative or computational field.
• Background in modelling disease biology, systems biology, molecular biology and biochemistry.
• Track record of delivering high-quality, research-level machine learning and robust software solutions.
• Peer-reviewed publications in major AI conferences.

The role is hybrid, requiring on-site work 2 days per week. Remote or fully home-working arrangements are not available for this role.

Please submit your CV and a short cover letter explaining how your experience maps to the role and what you hope to learn. We welcome applicants from a range of backgrounds and career stages. If you need an adjustment during the application process, tell us and we will support you.

GSK is an Equal Opportunity Employer. This ensures that all qualified applicants will receive equal consideration for employment without regard to race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), parental status, national origin, age, disability, genetic information (including family medical history), military service or any basis prohibited under federal, state or local law.