Conduct original research in computational protein engineering
Develop and deploy state-of-the-art ML methods for multi-objective, constraint-aware protein optimization
Apply proprietary computational framework and ML models for protein developability engineering
Communicate research findings through manuscripts, conference presentations, and internal seminars
Be an active member of a highly interdisciplinary team

Ph.D. degree in computational chemistry, physical or biological sciences, chemical engineering, computer science, or related discipline
Less than 2 years of post-degree experience
Willingness to make a minimum 2-year commitment
Candidates advancing to interview stage must provide two letters of recommendation
Successful record of scientific accomplishments evidenced by scientific publications and/or presentations with at least 2-3 first-author publication in a peer-reviewed journal
Strong familiarity with state-of-the-art protein engineering tools (i.e., RFDiffusion, ProteinMPNN)
Strong working knowledge of modern protein engineering and generative modeling approaches
Solid foundation in protein language models and structure-aware generative models
Hands-on experience with machine learning and computational biology libraries, including PyTorch (required) and RDKit (required)
Proficiency in Python programming
Familiarity with running compute-intensive machine learning experiments
Strong intellectual curiosity and enthusiasm for data-driven research

401(k) plan with Pfizer Matching Contributions and an additional Pfizer Retirement Savings Contribution
Paid vacation
Holiday and personal days
Paid caregiver/parental and medical leave
Health benefits to include medical, prescription drug, dental and vision coverage

Postdoctoral Fellow – Generative AI for Protein Engineering

Key skills