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Added 9 mins ago
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AI Biologist - Longevity
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San Francisco, CA
- $140,000 to $180,000 USD / year
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LatchBio

AI Biologist - Longevity

San Francisco, CA, United States

$140,000 to $180,000 USD / year

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Job details

Pay: $140,000 to $180,000 USD / year
Posting language: English
Posted: Nov 10, 2026

Original job description

AI Biologist - Longevity

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 to join our Longevity team, pushing what AI can achieve in aging biology.

About the Role. You will be the scientific authority on ground truth, testing whether AI agents can reason like an expert aging biologist. Today’s agents struggle to separate biological age from chronological age, spot confounds that make aging signals look real, and judge which longevity claims hold up. Working with real published and public datasets, you’ll build the tasks behind our benchmarks, starting with aging biomarkers and the hallmarks of aging. Your judgment sets the standard agents are held to.

Build the task

Turn real aging research questions into tasks, and write gold-standard answers an expert would defend
Curate data across molecular and population scales: DNA methylation, transcriptomics, proteomics, single-cell, longitudinal cohorts and biobanks
Build tasks around aging clocks, biological age prediction, senescence, epigenetic drift, and mitochondrial dysfunction
Partner with software engineers to turn your judgment into workflows

Set the hard calls

Test whether agents catch cell-type composition, batch effects, and age imbalance between groups
Test whether agents know when findings in worms, mice, or cell models won’t translate to human aging
Adjudicate contested longevity claims: what the data support, what they don’t, and what would settle it

Judge the agent

Challenge reasoning that sounds right but wouldn’t survive peer review or a go/no-go decision
Pinpoint where agents fail, what an expert would do instead, and what evidence would change the call
Reward calibrated uncertainty over confident guesses

Requirements

Master’s or PhD in biology, bioinformatics, computational biology, or a related field
Research in aging biology, or in a field where aging is studied: cancer biology, epigenetics, immunology, metabolism, mitochondrial biology, neurodegeneration, or epidemiology
Working knowledge of aging: the hallmarks, how clocks are built, and biological vs. chronological age
Depth in at least one of:
Computational: machine learning or statistics on omics data
Experimental: hands-on experience generating omics or aging data (sequencing, model organisms, senescence or mitochondrial assays), so you know how experiments go wrong
You code your own analysis end to end in Python or R
Work you drove yourself (first-author paper, thesis, independent project) that you can defend in detail
A track record of catching errors in data that others missed
Daily use of AI coding tools, and the confidence to overrule an AI when the evidence demands it

Strongly preferred

Published research in aging, geroscience, or longevity
Experience building or validating aging clocks, or analyzing lifespan or healthspan data
Familiarity with UK Biobank, TCGA, GEO, Tabula Muris Senis, or the NIA Interventions Testing Program

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

Apply. If your profile matches, we’ll send you a take-home project.
Take-home project (HackerRank). We review it as soon as it arrives.
Technical interview with a Member of Technical Staff (pod lead), scheduled right after your project review.
Culture interview with our C-suite.
Offer.
Background check, once you accept.

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More about this job

Skills

Research
Machine learning
Statistics
Python
R
Ice-skating

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