AI Biologist - Drug Development

latch.bio; San Francisco (CA); Remote; USD 140,000 - 180,000; Full time

Benefits offered by this job

Health coverage

401(k)

PTO 12 days

Job summary

Latch.bio is seeking an AI Biologist - Drug Development to advance AI-driven evaluation in drug discovery and development. You will join a team of software engineers and scientists building datasets and AI agent tasks across the end-to-end pipeline.

The role focuses on validating programs, interpreting PK/PD data, and ensuring pharmacological soundness of agent outputs with a bias toward defensible decisions.

Qualifications

• Advanced degree in pharmacology, toxicology, pharmaceutical sciences, medicinal chemistry, or related field.
• 3+ years hands-on pharma/biotech drug discovery/development experience.
• Experience with lead nomination or IND-enabling study design in at least one modality.

Responsibilities

• Contribute to evaluating agent performance across drug discovery and development pipeline.
• Build datasets and AI agent tasks for end-to-end drug development.
• Design program case studies from target validation to IND-enabling stages.

Skills

Drug discovery

Lead optimization

IND-enabling studies

PK/PD interpretation

Regulatory knowledge

Scientific writing

Education

Advanced degree in pharmacology/toxicology/related field

MS/PhD/PharmD or equivalent industry experience

Tools

Python

R

GraphPad Prism

Job description

AI Biologist - Drug Development

Latch builds rigorous scientific benchmarks (benchmarks.bio http://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.

In the US: full‑time W-2

• Guaranteed base set by your state, paid every paycheck no matter what
• Health, dental, vision: 100% covered for you, 50% for dependents
• 401(k) (no match)
• PTO: 12 days to start, more as you’re promoted

Outside the US: contractor

• Paid on performance, with a monthly advance of 60% of OTE that’s never clawed back. US benefits don’t apply.

Growth: two tracks, based on output, not tenure.

• Individual contributor: six levels, so up to five promotions, with OTE rising at each one.
• Management: after your first promotion, you can move into managing a pod if that’s what you want.

Location: fully remote is fine; onsite is preferred, at our Mission Bay waterfront office in SF with catered lunch and dinner and unlimited snacks.

Work authorization: we sponsor H-1B (new and transfers), O-1, and TN, and support STEM OPT.

Interview Process

2. Take‑home project (HackerRank). We review it as soon as it arrives.

3. Technical interview with a Member of Technical Staff (pod lead), scheduled right after your project review.

4. Culture interview with our C‑suite.

5. Offer.

6. Background check, once you accept.