# AI Biologist - Drug Development at LatchBio

LatchBio is hiring an AI Biologist - Drug Development for a remote role open to applicants in United States. It pays $140k-$180k a year and Level rates it Works on AI ●●●○; you can [apply on LatchBio](https://jobsbylevel.com/go/9f9adf81-fac9-457d-a7bb-e6729838b8a0?utm_medium=ai-agent).

AI Level 3, AI centrality 65 out of 100. Remote (San Francisco or Remote).

## Details

- Company: [LatchBio](https://jobsbylevel.com/companies/latchbio)
- AI level: AI Level 3 (score 65 out of 100)
- Location: Remote (San Francisco or Remote)
- Salary: $140k-$180k
- Posted: October 30, 2026
- Apply: https://jobsbylevel.com/go/9f9adf81-fac9-457d-a7bb-e6729838b8a0?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=ai-biologist-drug-development-at-latchbio-f548e5

## Prepare for this job

The job page lists 5 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/ai-biologist-drug-development-at-latchbio-f548e5

## Description

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 How we work. 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 respectfully opinionated, we respect each other’s time, and we think through possible solutions before we bring a problem to anyone. Most of the team arrives between 7 and 10am and leaves between 4 and 7pm. Nobody tracks when you’re in, but we give each other a heads-up on Slack when we’re running later than usual. We judge output, not time in a seat, and the only fixed meeting are a few pod check-ins per week. 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 offsites, and a community that does cycling, soccer, figure skating, boxing, running, martial arts, music, books, and game nights. Compensation & Logistics (US) Pay: $140K–$180K OTE, a mix of base &

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