Drimva

Isomorphic Labs

Research Leader / Senior Scientist (In vivo Pharmacology & Translational Sciences), 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, Immunology, Cancer Biology, Molecular Biology, or a related scientific discipline.

About the job

Leads translational pharmacology strategy and studies, connecting computational drug predictions with experimental evidence to advance small-molecule programs toward clinical development.

What you'll do

  • Plan and oversee target-specific in vitro and in vivo pharmacology, PK/PD, and safety assessments, including CRO studies.
  • Develop translational strategies, models, and biomarker plans to support patient selection, clinical proof of concept, and trial design.
  • Interpret experimental data and provide strategic recommendations for project decisions, clinical translation, and IND filings.

What they ask for

  • Ph.D. in Pharmacology, Immunology, Cancer Biology, Molecular Biology, 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 programs, ideally through clinical candidate delivery.
  • Expertise in translational models, TPPs and screening cascades, biomarker strategies, PK/PD, and managing CRO networks.

Nice to have

  • Experience with strategic de-risking and SAR strategies; good understanding of medicinal chemistry concepts.
  • General knowledge of machine learning modeling applied to biological systems and drug discovery.

Tools

in vivo, in vitro, PK/PD, CROs, ADMET, SAR, machine learning

AI focus

Provides biological expertise and experimental validation for ML-designed medicines, translates in silico predictions into physical validation protocols, and supplies data to improve predictive drug-design models.

Model-written summary · the original posting is authoritative

First seen Oct 29, 2026 · Last verified Nov 6, 2026 (UTC)