Drimva

Generate Biomedicines

Principal Scientist, Target Binding

$160K–$224K

Somerville, MA +1 · Principal · Full-time · Posted Oct 16, 2026

Location
Somerville, MA
Somerville, Massachusetts, United States
Experience
At least 8+ years of relevant industry experience.
Education
Ph.D. in biochemistry, biophysics, analytical chemistry, protein science, chemical biology, or a related discipline.

About the job

Leads target-binding strategy, assay development, and technology advancement across biotherapeutic programs while managing scientists and supporting cross-functional decisions.

What you'll do

  • Lead target-binding strategy and develop quantitative protein–protein interaction and cell-binding assays, including for challenging molecular systems.
  • Advance measurement technologies and establish standards for assay quality, data processing, reproducibility, and interpretation.
  • Partner across programs, communicate findings, and manage, mentor, and develop junior scientists and research associates.

What they ask for

  • Ph.D. in biochemistry, biophysics, analytical chemistry, protein science, chemical biology, or a related discipline.
  • At least 8+ years of relevant industry experience.
  • Extensive industry experience developing and applying quantitative protein–protein interaction and target-binding assays; expertise with multiple technologies, including SPR, BLI, KinExA, and cell-binding assays.
  • Experience leading scientific strategy across cross-functional programs and managing, mentoring, and developing scientists or research associates.

Nice to have

  • Experience with biotherapeutic analytics and developability assessment, laboratory automation, high-throughput experimentation, or computational data pipelines.
  • Proficiency with scripting, quantitative modeling, data science, or machine-learning workflows; practical experience with AI-enabled or agentic tools.

Pay

$160,000 — $224,000 USD per year base salary

Tools

SPR, BLI, KinExA, machine-learning

AI focus

Applies quantitative, statistical, machine-learning, and agentic approaches to improve experimental design, data analysis, troubleshooting, and scientific decision-making; preferred experience includes AI-enabled or agentic tools.

Model-written summary · the original posting is authoritative

First seen Oct 17, 2026 · Last verified Nov 5, 2026 (UTC)