Principal Scientist , Computational Sciences Protein Structure - CA

Job Description

ATTENTION MILITARY AFFILIATED JOB SEEKERS - Our organization works with partner companies to source qualified talent for their open roles. The following position is available to Veterans, Transitioning Military, National Guard and Reserve Members, Military Spouses, Wounded Warriors, and their Caregivers. If you have the required skill set, education requirements, and experience, please click the submit button and follow the next steps. Unless specifically stated otherwise, this role is "On-Site" at the location detailed in the job post.

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.

Additional Qualifications/Responsibilities

Required qualifications
• Ph.D. 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

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If you come across a role that intrigues you but doesn't perfectly line up with your resume, we encourage you to apply anyway. You could be one step away from work that will transform your life and career.

Compensation Overview:

San Diego - CA - US: $156,890 - $190,117