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