Working with Us. Challenging. Meaningful. Life-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams. Take your career farther than you thought possible. Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working-with-us. About the role. We are seeking an experienced, creative, and highly collaborative scientist to join our Biotherapeutics Computational Design team. In this role, you will build and deploy cutting edge machine learning and structure-based methods to accelerate biologics discovery across our preclinical portfolio (antibodies, multispecifics, ADCs, and novel scaffolds) and play a pivotal role in scaling an agentic antibody design platform from prototype to a core engine of research innovation. This position offers a unique opportunity to work at the intersection of machine learning, structural biology, and drug discovery. You will leverage large-scale proprietary and public datasets and collaborate with cross-functional teams of scientists to address complex challenges in biologics discovery and engineering. Your contributions will directly support and accelerate the development of next-generation therapies. At Bristol Myers Squibb, we are driven by a shared mission: to deliver innovative, life-changing medicines for patients facing complex diseases with unmet medical needs. If you are motivated by meaningful scientific challenges and the opportunity to make a real impact, we encourage you to join us. Key responsibilities - Develop and scale antibody design capabilities from prototype to application:Advance agentic antibody design approaches into robust, reusable workflows that support preclinical discovery efforts. - Build and apply state-of-the-art models for biologics design: Develop methods for protein structure modeling, binder design, affinity/specificity prediction, and developability property prediction using internal and external datasets. - Deliver reliable, production-ready research tools: Own end-to-end development of computational pipelines, with strong emphasis on reproducibility, benchmarking, and maintainable, well-documented code. - Lead through influence: Partner with computational and wet-lab teams to prioritize capabilities, translate insights into actionable decisions, and communicate clearly to technical and non-technical audiences. Required qualifications - PhD. in structural bioinformatics, computational biology, computer science, engineering, physics, or a related discipline, along with 4 or more years of relevant industry or academic experience - Expertise in modern machine learning approaches (e.g. transformers and diffusion/flow-based generative models) and strong fundamentals in classical machine learning methods - Experience developing and evaluating predictive models, including familiarity with model assessment, benchmarking, and experimental design - Hands-on experience with protein modeling approaches, including state-of-the-art methods for protein structure prediction and generative protein design - Experience developing or applying agentic AI frameworks to build applications that automate and accelerate research workflows - Strong Python skills and commitment to reproducible research and high-quality scientific software - Ability to identify high-impact problems, work independently to drive solutions through implementation and evaluation - Collaborate effectively across disciplines and communicate technical finding clearly through visualization, concise narratives, and actionable recommendation. Preferred qualifications - Experience with physics-based modeling (e.g. molecular dynamics, free energy perturbation) or closed-loop optimization methods (e.g. Bayesian optimization, active learning) - Background knowledge in biochemistry, protein engineering, or related experimental disciplines#LI-Hybrid.