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Open nowPosted 16 hours ago

AI Biologist - Drug Development

LatchBio26 open roles

Where
San Francisco or Remote
Work mode
Remote
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Your applicationOpen nowAI Biologist - Drug DevelopmentLatchBio · San Francisco or Remote

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  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

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The clock on this job

Early applications get read.

12.8% of postings close within 7 days. Measured by our own scanner across the market. LatchBio postings stay open a median of 49 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.5%3 days
  3. 12.8%7 days
  4. 20.6%14 days
  5. 35.6%30 days
  6. 58.4%60 days
This job: posted 16 hours ago

LatchBio median: 49 days open

The posting

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

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 & uncapped performance pay. Split varies by state.

- Performance pay is based on weekly quota attainment.

- If we pull you onto a special project, you’re paid full OTE for its duration.

- If the platform goes down, you’re paid full OTE for that day.

- Ramp: full OTE for your first 4 weeks while we onboard you into our workflows.

- Employment: full-time W-2

- Health, dental, vision: 100% covered for you, 50% for dependents

- 401(k) (no match)

- PTO: 12 days to start, more as you’re promoted

- Equity

- Growth: promotions are based on output, not tenure, with OTE rising at each level.

- Location: remote, hybrid, or onsite in SF. Onsite is preferred: waterfront office in Mission Bay, catered lunch and dinner, unlimited snacks.

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

Interview Process

Timeline: We move fast: no longer than 2 week interview process

1. Apply

2. Take-Home Project - HackerRank

3. Latch Intro - Talent Team

4. Technical Interview - Member of Technical Staff

5. Culture Interview - C-Suite

6. Offer

7. Background Check

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