Isomorphic Labs
Research Leader / Senior Scientist (In vitro / Cellular Pharmacology), Cambridge, MA
Cambridge, MA +1 · Senior · Full-time · Posted Jul 31, 2026
- Location
- Cambridge, MA
Cambridge, Massachusetts, United States - Experience
- 8+ years / 5+ years of professional experience in a Translational Sciences role; the posting does not specify which level corresponds to each threshold.
- Education
- Ph.D. in Pharmacology, Biochemistry, Cell Biology, Biophysics, or a related scientific discipline.
About the job
Lead in vitro and cellular pharmacology strategy and assays for small-molecule drug discovery programs, connecting experimental results with computational predictions to advance candidates toward clinical development.
What you'll do
- Design screening cascades and develop biophysical, biochemical, and cellular assays to assess target interactions, potency, specificity, and mechanism.
- Oversee CRO experiments and interpret biological data to guide project decisions and support IND-enabling packages.
- Collaborate with AI/ML, data science, medicinal chemistry, structural biology, and translational teams; some roles also manage and mentor a team of 2–3 pharmacologists.
What they ask for
- Ph.D. in Pharmacology, Biochemistry, Cell Biology, Biophysics, or a related scientific discipline.
- 8+ years / 5+ years of professional experience in a Translational Sciences role within biotech, pharma, or a CRO setting.
- Experience leading biology or projects in small-molecule drug discovery; extensive experience designing and implementing biological screening cascades.
- Deep knowledge of in vitro pharmacology assays and experience working with CROs; ability to guide cross-functional programs.
Nice to have
- Experience partnering with Data Science groups to establish biological data ingestion systems.
- Experience with alternative modalities or chemistries, or general knowledge of machine learning applied to biological systems and drug discovery.
Tools
SPR, ITC
AI focus
Translate in silico predictions into testable biological hypotheses, provide experimental data loops to train future models, and collaborate with AI/ML and data science teams.
Model-written summary · the original posting is authoritative