AI Biologist - Drug Development

Role overview Assess how well AI agents reason through real drug development decisions, from validating a target to preparing a candidate for clinical testing. You’ll work with scientists and software engineers to build realistic case studies and datasets, then judge whether agent recommendations are pharmacologically sound when evidence is incomplete or conflicting.

Responsibilities - Design drug development case studies covering target validation, lead optimization, candidate selection, and studies that support an investigational application. - Assemble and interpret preclinical evidence, including potency and selectivity results, ADME and pharmacokinetic profiles, efficacy, toxicology, safety pharmacology, and biodistribution. - Reconstruct program decisions from information across sources and assess whether the evidence supports advancing or stopping a candidate. - Review AI agent responses for scientific and practical soundness, including confidence when the evidence is uncertain. - Work with software engineers and scientists to develop datasets and evaluation tasks across drug development.

Requirements - Advanced training in pharmacology, toxicology, pharmaceutical sciences, medicinal chemistry, or a related biomedical field, or equivalent industry experience. - At least three years of hands-on pharma or biotech drug discovery or development experience. - Direct experience with lead optimization, candidate nomination, or IND-enabling study design in at least one modality. - Ability to interpret PK/PD, ADME, and GLP toxicology data for dose selection and risk assessment, and to evaluate SAR, potency, selectivity, and developability trade-offs. - Working knowledge of pharmacology and toxicology studies such as safety pharmacology, genotoxicity, biodistribution, and bioanalytical assays. - Clear scientific writing, sound judgment when reviewing AI outputs, initiative, and comfort identifying gaps in uncertain data.

Nice to have - Familiarity with IND-enabling regulatory requirements and nonclinical packages expected by the FDA or EMA.

Benefits and work setup - Full-time employment with remote, hybrid, or onsite options; onsite work in San Francisco is preferred. - Listed compensation is $140,000–$180,000 OTE, combining base pay and uncapped performance pay. The listing also describes a four-week OTE ramp, health, dental, and vision coverage, retirement plan, paid time off, and equity. - Work is judged by output, with flexible hours and a few fixed team check-ins each week.