Senior / Principal Scientist, Antibody Engineering & Rational Design

Position Description

We are seeking an innovative Senior Scientist / Principal Scientist to bridge the gap between traditional antibody discovery and cutting-edge computational design. In this role, you will serve as the primary scientific liaison between the wet-lab antibody team and our machine learning team, driving the evolution of our "Design-Build-Test-Learn" cycle.

You will be responsible for using internal and external computational tools to generate designs for a variety of discovery campaigns and optimization of antibodies. You will also ensure that the computational predictions are rigorously validated and that the resulting biological data is structured to 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.

Position Requirements

• Ph.D. in molecular biology, biochemistry, biophysics, computational chemistry, structural biology, or related discipline
• 5+ years' (for Senior Scientist) or 8+ years (for Principal Scientist) post-PhD experience in antibody/protein engineering within the biotech or biopharma industry
• Demonstrated ability to work closely with data scientists and ML engineers and to articulate biological constraints (e.g., immunogenicity, manufacturability) to collaboratively improve model performance
• Hands-on experience using state of the art ML protein design tools (e.g. AlphaFold, Boltz, RFDiffusion, ProteinMPNN)
• Fluency with programming in Python
• Extensive experience in rational design for humanization, affinity/specificity optimization, pH-dependence, and liability removal
• Strong understanding of biophysical characterization to ensure designed molecules are developable therapeutics
• Experience technically managing CROs for gene mutagenesis, in vitro library design, protein production, and screening assays
• Ability to independently propose novel scientific approaches and solve complex technical hurdles
• Must be willing to work onsite 5 days a week

Nice to Have

• Experience with structural modeling using molecular dynamics or other physics-based simulation (e.g. Schrödinger, MOE, Rosetta) to validate and optimize designs
• Experience designing experiments specifically to generate high-quality training data for ML models (e.g. focused libraries or systematic mutagenesis for optimization of binding, manufacturability, biophysical properties and stability)
• Experience running computational pipelines for de novo antibody or VHH design
• Familiarity with common scientific computing and machine learning Python libraries (e.g. NumPy, SciPy, PyTorch)
• Experience with in vivo and in vitro discovery of antibodies by display technologies
• Experience in engineering multi-specific antibodies

Pay and benefits

The estimated base salary range for this role is $177,000 - $235,000. Actual pay will be based on a number of factors including experience and qualifications. This position is also eligible for two annual cash bonuses.