Principal Scientist, Computational Sciences - Protein Structure Prediction and Design
Bristol Myers Squibb
9 hours ago
Remote friendly (San Diego, CA)
United States
Clinical Research and Development
Key Responsibilities:
- Develop and scale antibody design capabilities from prototype to application; advance agentic antibody design into robust, reusable workflows for preclinical discovery.
- Build and apply state-of-the-art models for biologics design: protein structure modeling, binder design, affinity/specificity prediction, and developability prediction using internal/external datasets.
- Deliver reliable, production-ready computational research tools: end-to-end pipeline development focused on reproducibility, benchmarking, and maintainable well-documented code.
- Lead through influence: partner with computational and wet-lab teams to prioritize capabilities, translate insights into decisions, and communicate clearly to technical and non-technical audiences.
Required Qualifications:
- Ph.D. in structural bioinformatics, computational biology, computer science, engineering, physics, or related field; 4+ years relevant industry/academic experience.
- Expertise in modern ML (e.g., transformers, diffusion/flow-based generative models) and classical ML fundamentals.
- Experience developing/evaluating predictive models; familiarity with assessment, benchmarking, and experimental design.
- Hands-on protein modeling (protein structure prediction and generative protein design).
- Experience developing/applying agentic AI frameworks to automate and accelerate research workflows.
- Strong Python skills; commitment to reproducible research and high-quality scientific software.
- Ability to identify high-impact problems and drive solutions through implementation and evaluation.
Preferred Qualifications:
- Physics-based modeling (e.g., molecular dynamics, free energy perturbation) or closed-loop optimization (e.g., Bayesian optimization, active learning).
- Background in biochemistry, protein engineering, or related experimental disciplines.
Benefits (as listed):
- Health coverage (medical, pharmacy, dental, vision), wellbeing support, financial wellbeing/protection (incl. 401(k), insurance).
- Paid Time Off and additional time-off options based on eligibility.
Application instruction:
- If the role isn’t a perfect match, apply anyway.
- Develop and scale antibody design capabilities from prototype to application; advance agentic antibody design into robust, reusable workflows for preclinical discovery.
- Build and apply state-of-the-art models for biologics design: protein structure modeling, binder design, affinity/specificity prediction, and developability prediction using internal/external datasets.
- Deliver reliable, production-ready computational research tools: end-to-end pipeline development focused on reproducibility, benchmarking, and maintainable well-documented code.
- Lead through influence: partner with computational and wet-lab teams to prioritize capabilities, translate insights into decisions, and communicate clearly to technical and non-technical audiences.
Required Qualifications:
- Ph.D. in structural bioinformatics, computational biology, computer science, engineering, physics, or related field; 4+ years relevant industry/academic experience.
- Expertise in modern ML (e.g., transformers, diffusion/flow-based generative models) and classical ML fundamentals.
- Experience developing/evaluating predictive models; familiarity with assessment, benchmarking, and experimental design.
- Hands-on protein modeling (protein structure prediction and generative protein design).
- Experience developing/applying agentic AI frameworks to automate and accelerate research workflows.
- Strong Python skills; commitment to reproducible research and high-quality scientific software.
- Ability to identify high-impact problems and drive solutions through implementation and evaluation.
Preferred Qualifications:
- Physics-based modeling (e.g., molecular dynamics, free energy perturbation) or closed-loop optimization (e.g., Bayesian optimization, active learning).
- Background in biochemistry, protein engineering, or related experimental disciplines.
Benefits (as listed):
- Health coverage (medical, pharmacy, dental, vision), wellbeing support, financial wellbeing/protection (incl. 401(k), insurance).
- Paid Time Off and additional time-off options based on eligibility.
Application instruction:
- If the role isn’t a perfect match, apply anyway.