---
title: 'AI Biologist - Peptide Discovery at LatchBio'
canonical: 'https://feeny.ai/job/ai-biologist-peptide-discovery-latchbio-san-francisco-bwsq5fqxykx6'
type: 'job'
last_seen: '2026-11-03'
---

# AI Biologist - Peptide Discovery at LatchBio

- **Company:** LatchBio
- **Location:** San Francisco, CA
- **Compensation:** $140k–$180k
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-11-02
- **Last confirmed live:** 2026-11-03
- **Apply:** https://jobs.ashbyhq.com/latchbio/b60fddff-1079-487e-808f-accdff3522f6

## Job description

AI Biologist - Peptide Discovery 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 Peptide Discovery team, pushing what AI can achieve in therapeutic peptide discovery.

About the Role. You will be the scientific authority on ground truth, testing whether agents can reason like an expert peptide discovery scientist. AI agents today struggle to interpret raw selection, biophysical, and structural data, to separate true hits from artifacts, and to make the calls that move a peptide from target to candidate. The data often misleads: enrichment can be a propagation artifact, protection is not contact, and binding is not function. Knowing what the evidence actually supports is the work. You will work with software engineers and scientists to build the tasks and datasets behind our benchmarks. They span linear, macrocyclic, and constrained peptides against protein targets, including protein–protein interactions, from target tractability to candidate nomination. Your judgment will set the standard agent outputs are held to, including:

Build the task

- Define what expert reasoning looks like for a peptide discovery question, and write gold-standard answers with the rationale an experienced peptide scientist would defend
- Design realistic scenarios across the discovery arc: target tractability, hit generation, hit confirmation and kinetics, binding-site mapping, functional confirmation, and lead optimization
- Curate datasets from real programs: display-selection read counts, SPR/BLI sensorgrams, fluorescence-polarization titrations, affinity-selection MS, HDX-MS, crosslinking-MS, NMR chemical-shift perturbation, dose-response, and permeability and stability data
- Build analysis pipelines in Python, from enrichment analysis to kinetic and dose-response fitting, that produce verifiable ground truth with tolerances that hold across replicates and fitting routines
- Partner with software engineers to turn your judgment into new workflows

Set the hard calls

- Test whether agents distinguish true signal from artifact, such as propagation or expression artifacts enriched across rounds, sensorgrams distorted by aggregation or superstoichiometric binding, or HDX protection mistaken for direct contact
- Adjudicate conflicting evidence: reconcile orthogonal binding-site datasets, decide between competitive and allosteric engagement, and identify which experiment would settle the call
- Test whether agents weigh the right tradeoffs: binding versus function, potency versus developability, and when added constraint or affinity costs a property that matters more

Judge the agent

- Interrogate agent reasoning step by step, and challenge conclusions that sound right but would not survive a project review or lab meeting
- Pinpoint where agents go wrong: why a step fails, what an expert would do instead, and what evidence would change the call
- Score agent confidence, rewarding explicit uncertainty over overconfident calls on ambiguous data

## Requirements

- PhD in peptide chemistry, biochemistry, biophysics, structural biology, medicinal chemistry, or a related field
- Senior, hands-on peptide discovery experience: you have personally analyzed real program data, not just supervised it
- Direct experience with at least one of: display selection (phage, mRNA, ribosome, RaPID, or DNA-encoded libraries), SPR/BLI or FP binding kinetics, binding-site mapping by structural MS or NMR, or peptide developability (PAMPA/Caco-2 permeability, proteolytic and serum stability)
- Deep expertise in at least one area (display and selection, biophysics, structural mapping, or peptide medicinal chemistry) and working fluency across the others
- Strong command of experimental design, quantitative data analysis in Python or R, and statistics
- AI-ready from day one: daily use of Claude, Cursor, or similar agents, with the judgment to spot where model reasoning is technically correct but scientifically unsound
- Confidence to overrule an AI agent when the evidence demands it, and the openness to update when shown wrong
- Ability to calibrate confidence and communicate uncertainty rather than defaulting to binary calls
- Bonus: peptide format and chemistry experience, such as macrocyclization, hydrocarbon stapling, N-methylation, D- and non-canonical residues, or HELM encoding
- Bonus: target tractability or de novo design experience, such as PPI hotspot analysis, human-genetic evidence for therapeutic direction, or computational design with Rosetta or RFdiffusion

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.

## About LatchBio

## Company Overview
- **One-liner**: LatchBio builds and deploys frontier AI agents for biological data analysis, serving over 300 biopharma and R&D labs worldwide.
- **Entity Type**: Private (Series A; total funding $33.2M)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2021
- **Founders**: Alfredo Andere (CEO) and Kenny Workman (CTO)

## Core Business
- Primary industry: Biotechnology Research / AI for biology
- Target customers: B2B – biopharma companies, academic R&D labs, and solution providers (kits, assays, instrument vendors)
- Mission: Deploy AI agents into real scientific work to accelerate data analysis and drug discovery

## Products & Services
- **Latch Platform**: A unified, serverless bioinformatics platform that lets scientists run, track, and debug pipelines via an SDK-generated no-code interface. Supports FastQ, CSV, H5AD, TIFF, and other formats. Agents can analyze messy real-world data.
- **Latch SDK**: Python toolchain to define serverless workflows and dynamically generate no-code UIs, removing cloud-infrastructure overhead.
- **agent.bio**: Public, interactive sandbox where frontier AI agents (e.g., Claude 4.8 Opus) analyze biological data. Supports 40+ solution providers.
- **benchmarks.bio**: Open, reproducible benchmarks measuring AI agent performance on spatial, single-cell, epigenomics, and preclinical pharmacology data. Includes papers like scBench-Long, TxBench-PP, etc.
- **White-labeled data analysis**: Solution providers can offer a branded, simpler analysis experience for their kits and services.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Key Metric**: Annual revenue of ~$1.0M (LinkedIn estimate); total funding $33.2M
- **Notable Investors/Partners**: Coatue, Lux Capital, Fifty Years, Haystack, Gelt, Backend Capital; individual investors include George Church, Jeff Dean, Hemant Taneja, Laura Deming, Alec Nielsen
- **Growth Signals**: 140% YoY headcount growth (from ~11 to 26 employees); 300+ biopharma and academic teams served; 40+ solution providers onboarded; recent publication of multiple benchmark papers (2025–2026)

## Competitive Advantages
- **First-mover in AI agents for biology**: Combines frontier LLMs with rigorous, verifiable benchmarks tailored to biological data (spatial, single-cell, epigenomics, pharmacology).
- **Open benchmarks & reproducibility**: All benchmarks (e.g., scBench-Long) are published openly, building trust and attracting top talent.
- **White-labeling for solution providers**: Allows kit and instrument vendors to offer AI-powered data analysis under their own brand, creating a network effect.
- **Tight integration with real lab workflows**: Supports common file formats and interfaces with existing pipelines, reducing friction for scientists.

## Strategic Focus
- Continue research and deployment of AI agents for biology, with emphasis on long-horizon tasks and messy real-world data.
- Expand the agent.bio sandbox to more solution providers and biopharma customers.
- Publish additional benchmarks (spatial, preclinical) to establish Latch as the standard evaluator of AI in biology.
- Grow engineering and bioinformatics teams to accelerate platform development.

## Why Work Here
- **Engineering culture**: Small, high-autonomy team where engineers own features end-to-end and see their code impact thousands of scientists the same day. Reading groups on category theory, immunology, and more.
- **Remote/hybrid policy**: Not explicitly stated, but headquarters in San Francisco; office parking subsidized. Likely in-office with flexibility.
- **Notable perks**: Unlimited PTO, 100%-subsidized platinum health plan (Cigna) + 75% for dependents, dental/vision, 401(k), ambitious equity, 12+ meals weekly, annual tech stipend, signing bonus, O-1 visa sponsorship, professional development.
- **Team size**: ~26 employees, with 65% in technical roles (engineering, bioinformatics, research). Flat structure with direct access to founders.
- **Growth trajectory**: Rapid headcount growth and expanding customer base offer career acceleration opportunities.

## Sources
1. [latch.bio](https://latch.bio/)
2. [latch.bio/about](https://latch.bio/about)
3. [linkedin.com/company/latchbio](https://www.linkedin.com/company/latchbio)
4. [cbinsights.com/company/latch-bio](https://www.cbinsights.com/company/latch-bio)
5. [jobs.ashbyhq.com/latchbio](https://jobs.ashbyhq.com/latchbio)

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