Calico-posted about 8 hours ago
$198,000 - $241,000/Yr
Full-time • Principal
Onsite • South San Francisco, CA
101-250 employees

We are seeking an innovative Principal Scientist to bridge the gap between computational design and experimental validation in our antibody discovery engine. In this role, you will serve as the primary scientific liaison between our wet-lab antibody team and our machine learning team, driving the evolution of our "Design-Build-Test-Learn" cycle. Acting as both a project leader and technical expert, you will translate complex in silico designs into actionable experimental workflows. You will be responsible for ensuring that computational predictions are rigorously validated and that the resulting biological data is structured to actively retrain and refine our internal models. We are looking for a deep expert in therapeutic antibody development who is eager to move beyond traditional random screening and champion a rational, data-driven approach to biologic drug discovery.

  • Ph.D. in molecular biology, biochemistry, computational modeling, structural biology, or related discipline
  • 8+ years’ post-PhD experience in antibody/protein engineering within the biotech or biopharma sectors
  • Demonstrated ability to work intimately with data scientists and ML engineers and able to articulate biological constraints (e.g., immunogenicity, manufacturability) to computational teams and interpret their in silico outputs for experimental validation
  • Hands-on proficiency with structural modeling and extensive knowledge in ML protein design tools
  • Expert knowledge of antibody structure-function relationships
  • Extensive experience in rational design for humanization, affinity maturation, pH-dependence, and liability removal
  • Deep expertise in engineering multi-specific antibodies
  • Strong understanding of biophysical characterization to ensure designed molecules are developable therapeutics
  • Experience technically managing CROs for gene synthesis, protein production, and functional assays
  • Ability to propose novel scientific approaches and solve complex technical hurdles independently
  • Must be willing to work onsite 5 days a week
  • Experience designing experiments specifically to generate high-quality training data for ML models (e.g., focused libraries, systematic mutagenesis) is highly desirable
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