Scientist, Computational Protein Design, San Francisco Bay Area, CA

Role:
Scientist, Computational Protein Design

Adimab is the leading technology provider for therapeutic antibody drug discovery, focusing solely on our partnerships without pursuing an internal product pipeline. Since 2009, we have partnered with over 140 pharmaceutical and biotechnology companies, generating more than 650 therapeutic programs, of which more than 85 have entered clinical trials.

The role is on Adimab's computational biology team in Mountain View, CA. Data‑driven approaches have been central to the development of the Adimab platform, and the team is actively utilizing and developing modern de novo protein design and generative AI methods to extend its capabilities. You will serve as the computational lead for protein design campaigns, embedded within a world‑class team of modeling and wet‑bench scientists, with direct access to Adimab's industry‑leading experimental capabilities to drive the design‑build‑test cycle.

• Take end‑to‑end ownership of computational protein design campaigns — from design generation through wet‑lab collaboration, analysis of experimental data, and optimization of the design‑build‑test cycle. Applications span de novo epitope‑targeted IgG, VHH, and minibinder design, as well as protein solubilization and stabilization.
• Partner with wet‑lab teams to design experiments that generate custom training data for affinity, epitope, and specificity prediction models. Train and rigorously benchmark resulting models against internal and external baselines.
• Build and maintain the computational infrastructure supporting both protein design campaigns and model development, including reproducible pipelines and integration of computational outputs with wet‑lab data.
• Track developments in computational protein design and ML; evaluate relevance to Adimab's platform and identify opportunities for integration.
• Serve as a resource for wet‑lab scientists on AI/ML capabilities and best practices, helping antibody and protein engineering teams apply computational design methods.

• PhD in Biophysics, Biochemistry, Structural Biology, Computational Biology, or a related field.
• 2–4 years of post‑PhD experience specifically in computational protein or binder design.
• Strong foundation in the analysis of structural and energetic factors driving protein‑protein interactions.
• Proficiency with structure prediction and generative design tools such as RFAntibody, Bind Craft, and Protenix. Crystallography or cryo‑EM experience is a plus.
• Strong Python skills and experience building reproducible analysis and modeling pipelines.
• Proven track record of publication or patent contribution in applied ML for proteins or computational design.

We offer individually tailored compensation packages comprised of a competitive salary, meaningful equity, a 2:1 401(k) match, and comprehensive health care benefits.