Research Leader / Senior Scientist (In Vitro / Cellular Pharmacology) (AI Drug Discovery)

Location: Cambridge, Massachusetts, United States; hybrid work with office attendance required 3 days per week

Company

hirify.global applies frontier AI and machine learning to drug discovery, building predictive and generative models to design novel medicines and understand biological phenomena.

What you will do

• Design biological screening cascades for internal and partnered small-molecule drug discovery programs.
• Guide biophysical and biochemical assay development, including SPR, ITC, and enzymatic methodologies.
• Develop and refine functional and phenotypic cellular assays for target validation and mechanistic understanding.
• Work with CROs to design decision-making experiments and deliver high-quality biological data.
• Interpret binding, kinetic, potency, selectivity, and dose-response data and prepare technical documentation for strategic decisions and IND-enabling packages.
• Collaborate with AI/ML, Data Science, Medicinal Chemistry, Structural Biology, In Vivo Pharmacology, and Translational Sciences teams to connect computational predictions with experimental validation.

Requirements

• Ph.D. in Pharmacology, Biochemistry, Cell Biology, Biophysics, or a related scientific discipline.
• At least 8 years of experience for the Research Leader level or 5 years for the Senior Scientist level in Translational Sciences within biotech, pharma, or a CRO.
• Experience leading biology or projects in small-molecule drug discovery from inception through clinical candidate delivery.
• Extensive experience designing and implementing screening cascades from hit identification through candidate selection.
• Deep knowledge of in vitro pharmacology, including biophysical, biochemical, and/or cellular assays, plus CRO collaboration.
• Ability to provide technical guidance and communicate effectively across matrixed, interdisciplinary teams.

Nice to have

• Experience partnering with Data Science teams to establish biological data ingestion systems.
• Track record of contributing to programs that delivered clinical candidates.
• Experience with alternative modalities or chemistries such as peptides or molecular glues.
• Knowledge of machine learning applied to biological systems and drug discovery.

Culture & Benefits

• Collaborative, interdisciplinary environment spanning drug discovery and machine learning.
• Values centered on thoughtful, brave, determined, and collaborative scientific work.
• Shared learning, employee support, and an inclusive workplace.
• Hybrid working model with regular in-person collaboration.