Staff AI/ML Engineer - Controllable Biology

Business Introduction 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. We’re uniting science, technology, and talent to get ahead of disease together. Find out more: Our approach to R&D Job Title : Staff AI/ML Engineer - Controllable Biology At GSK we see a world in which advanced applications of machine learning and AI will allow us to develop novel therapies for existing diseases and to quickly respond to emerging or changing diseases with personalized drugs, driving better outcomes at reduced cost with fewer side effects. It is an ambitious vision that will require the development of products and solutions at the cutting edge of machine learning and AI. If that excites you, we'd love to chat. Job Purpose: The AI/ML Controllable Biology Team applies machine learning and AI methods to biological network s and sequence data from large-scale human genetic, functional genomic and single - cell experiments. Models of control of biological net works have the potential to be transformative in drug discovery, empowering us to find new life - saving medicines. We are looking for a Staff AI/ M L Engineer – Controllable Biology . Competitive candidates will have a track record in developing SOTA deep learning models for solving challenging real world scientific problems. You should be an outstanding scientist with in-depth knowledge in modern machine learning. You can convert vaguely described biological/drug discovery challenges into well-defined machine learning problem s . You can independently execute and deliver full AI/ML driven solution from sourcing training data, design and implementing SOTA machine learning models, testing, benchmark ing and product driven research for model performance improvement, to 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: As a Staff AI/ML Engineer – Controllable Biology you will: Convert complex biological questions into tractable math ematics problems that can be solved by modern computation al methods . Be adept a t decomp o sing large problems in to quarterly , measurable results and consistent ly 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 organization . 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 organi s ation . Partner with stakeholders to ensure models are explainable, safe, and relevant. Basic Qualifications: Master ’ s d egree in a related field ( e.g. computer science, mathematics or natural science s ) . 5+ years ’ experience with standard deep learning algorithms, model architectures, machine learning best practices, scalable training and deployment. 5+ years ’ experience in s oftware development, including code reviews, version control systems, CI/CD pipelines, software testin