Research Leader / Senior Scientist (In Vivo Pharmacology & Translational Sciences)

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

Company

hirify.global applies artificial intelligence and machine learning to drug discovery, building predictive and generative models to design molecules and develop new medicines.

What you will do

• Plan and implement target-specific in vitro and in vivo pharmacology, PK/PD, and safety assessments across small-molecule drug discovery portfolios.
• Design translational strategies, screening cascades, biomarker plans, and model paradigms to support clinical translation and early proof of concept.
• Lead decision-making experiments with CRO partners, including study design, contract management, timelines, and data-quality oversight.
• Analyze experimental data and provide recommendations for project leaders, partners, and cross-disciplinary teams to guide go/no-go decisions and IND filings.
• Translate machine-learning and computational biology predictions into experimental validation protocols and clean feedback datasets for model improvement.
• Collaborate with discovery biology, medicinal chemistry, DMPK, toxicology, clinical development, and program management teams to support candidate selection and first-in-human trial design.

Requirements

• Ph.D. in Pharmacology, Immunology, Cancer Biology, Molecular Biology, or a related scientific discipline.
• 5+ or 8+ years of professional Translational Sciences experience in a biotech, pharma, or CRO setting, depending on level.
• Experience leading biology or projects within small-molecule drug discovery programs from inception through clinical candidate delivery.
• Expertise in translational models for oncology and/or immune disorders, Target Product Profiles, screening cascades, biomarker strategies, and clinical trial design.
• Experience designing PK/PD studies for human dose prediction and coordinating translational biology with ADMET, formulation, and clinical specialists.
• Strong project leadership, communication, and matrix-team influence skills, including management of complex external studies and multi-vendor CRO networks.

Nice to have

• Experience with strategic de-risking, SAR strategies, medicinal chemistry, and DMPK collaboration.
• General knowledge of machine-learning modeling applied to biological systems and drug discovery.

Culture & Benefits

• Collaborative, interdisciplinary environment spanning machine learning, computational biology, and drug discovery.
• Values centered on thoughtful science, initiative, integrity, determination, and cross-disciplinary collaboration.
• Support for employee development, shared learning, and an inclusive workplace.
• Some positions include line management and mentoring of a team of 2–3 in vivo pharmacologists.
• Work follows a hybrid model with regular in-person collaboration.