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 : 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 - c ell experiments. Models of control of biological networks have the potential to be transformative in drug discovery, empowering us to find new life - saving medicines. We're looking for a highly accomplished AI/ML Engineer to help us make this vision a reality. Competitive candidates will have a breadth of knowledge across m achine l earning m ethods, as well as depth in at least one area . You can execute and deliver full AI/ML driven solution s from sourcing training data, design ing and implementing SOTA machine learning models, testing and benchmarking . 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. Basic Qualifications: Master ’ s d egree in a related field ( e.g. computer science, mathematics or natural science s ) . K nowledge of machine learning, software engineering and agentic development best practices . Proficiency with standard deep learning algorithms and model architectures. Experience with Python and PyTorch , or equivalent languages and machine learning frameworks. Experience working with biological sequence and network data, including genomics, transcriptomics, proteomics, gene regulatory networks or related biological datasets. Preferred Qualifications : If you have the following characteristics, it would be a plus : Advanced degree ( M aster's or PhD) in a quantitative or computational field. Knowledge of systems biology , disease biology, molecular biology and biochemistry . Experience with biological data (e.g., genomics, transcriptomics, epigenomics, proteomics, biological n etwork data ) . Peer-reviewed publications in major AI conferences. Track record of contributing to open-source projects. #GSK-LI Work Arrangements: The role is h ybrid , requiring on-site work 2 days per week. Remote or fully home-working arrangements are not available for this role. How to apply Please submit your CV and a short cover letter that explains 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 work with you. We look forward to hearing from you. Skills Artificial Intelligence (AI), Artificial Intelligence Ethics, Classification Models, Deep Learning, Intelligent Automation (IA), Machine Learning (ML), Model Evaluation, Model Validation, Predictive Modeling, Probabilistic Modeling, Python (Programming Language), Test Documentation