AI Biologist - Drug Development

Latch builds rigorous scientific benchmarks (benchmarks.bio) for AI agents across biology. We work with frontier labs and pharma to measure whether agents can handle real biological workflows: the messy, multi-layer reasoning that matters in the field.

We're seeking an AI Biologist - Drug Development to join our team working at the frontier of what artificial intelligence can achieve in drug discovery and development.

About the Role

You will contribute to our technical approach for evaluating agent performance at reasoning across the drug discovery and development pipeline, from target validation through IND-enabling studies, and translating preclinical data into the decisions that advance (or kill) a program.

You will work as part of a team of software engineers and scientists building datasets and AI agent tasks across the end-to-end drug development pipeline, including:

Designing realistic program case studies spanning target validation, lead optimization, candidate nomination, and IND-enabling studies across modalities

Curating multi-modal preclinical data bundles: SAR/potency panels, ADME/PK profiles, in vivo efficacy, GLP toxicology, safety pharmacology, and biodistribution data

Evaluating PK/PD, GLP toxicology, and safety pharmacology findings across modalities and development stages

Reconstructing program go/no-go decisions from multi-source data

Reviewing and stress-testing agent outputs for pharmacological soundness, catching reasoning that is textbook-correct but not defensible in a program review

Calibrating AI agent confidence on preclinical data with incomplete or conflicting signals, rather than forcing a binary call

Requirements

Advanced degree (MS/PhD/PharmD) in pharmacology, toxicology, pharmaceutical sciences, medicinal chemistry, or a related biomedical field, or equivalent industry experience

3+ years of hands-on experience in pharma or biotech drug discovery/development

Direct experience with lead optimization, candidate nomination, or IND-enabling study design in at least one modality

Experience interpreting PK/PD, ADME, and GLP toxicology data to inform dose selection and risk assessment

Familiarity with IND-enabling regulatory requirements and the nonclinical data package expected by FDA/EMA is s plus

Working knowledge of core pharmacology/tox study types: GLP tox, safety pharmacology, genotoxicity, biodistribution, bioanalytical assays

Experience evaluating SAR, potency, selectivity, and developability trade-offs across candidate molecules

Strong scientific writing skills, able to produce clear, evidence-supported rationale for a drug development decision

Proficiency in AI agent use, with the judgment to identify where model reasoning is technically correct but pharmacologically or practically unsound

Comfort operating in uncertainty, and proactively flagging gaps in data or instructions

Works with high agency and initiative

Culture @ Latch

We practice radical transparency: you’ll know how the company is doing, what’s working, and what isn’t. It’s always us against the problem, never each other. We’re opinionated but respectful of each other’s time, and we think through possible solutions before bringing a problem to anyone. We use AI constantly, so we reward original expert judgment, not AI output passed off as your own. You’ll start on the bench that fits your expertise and can move across teams as you build a track record.

Outside of work: company outings most months, team off-sites, and a community that does cycling, soccer, figure skating, boxing, running, martial arts, music, books, and game nights.

Compensation & Logistics

Pay: $140K–$180K OTE (on-target earnings), with no ceiling.

Your pay is your OTE times your weekly quota attainment. Hit quota, earn your OTE; double it, earn double. Example: $150K OTE at double quota = $300K.

Your first 4 weeks (ramp) are paid at full OTE while we train you.

You’re paid full OTE whenever quota can’t fairly apply: when a new project is starting, the platform goes down, or we pull you onto a special project.