Senior Scientist - Computational Protein Design

Key Responsibilities

• Develop and automate ML protein design workflows for binder generation
• Build scalable pipelines for designing large libraries of de novo proteins across multiple targets
• Apply computational methods to support multiplexed screening strategies and receptor discovery efforts
• Design and prioritize binders for individual targets and large target panels
• Contribute to shared tools, workflows, and best practices within the protein design community
• Collaborate closely with experimental scientists and data teams to enable rapid validation and iteration

Basic Qualifications

• Doctorate degree PhD
• OR PharmD
• OR MD [and relevant post-doc experience in Computational Biology, Structural Biology, Bioengineering, Biophysics, Computer Science, or related discipline with a focus on protein design]
• OR Masters degree and 3 years of protein design experience
• OR Bachelors degree and 5 years of protein design experience

Preferred Qualifications

• Ph.D. with postdoc in Computational Biology, Structural Biology, Bioengineering, Biophysics, Computer Science, or related discipline with a focus on protein design.
• Demonstrated experience in computational protein design, including the design of binders such as minibinders and/or antibodies.
• Experience developing automated and scalable computational pipelines for protein design or structural modeling, ideally in high-throughput environments.
• Familiarity with modern AI/ML-driven protein design and structure prediction tools (e.g., AlphaFold, ProteinMPNN, RFdiffusion, or similar frameworks).
• Strong programming skills in Python and/or other scripting languages, with experience building maintainable workflows and automation for large-scale computational experiments.
• Experience working with large protein libraries or multiplexed design strategies, including design filtering, ranking, and diversity optimization.
• Knowledge of protein structure-function relationships, epitope targeting strategies, and protein-protein interaction design principles.
• Experience integrating computational design outputs with experimental validation workflows, including library generation, screening, or directed evolution approaches.
• Familiarity with cloud computing, high-performance computing (HPC), and workflow orchestration tools.
• Experience collaborating in cross-functional teams spanning computational scientists, experimental biologists, and data scientists.
• Strong communication skills and ability to contribute to a collaborative protein design community, including sharing tools, best practices, and design insights.