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Job description
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 to join our Protein Design & Engineering team, working at the frontier of what artificial intelligence can achieve in biology.
About the Role
AI agents can now run the full protein design toolkit: generate backbones, design sequences, predict complexes, score interfaces and propose mutations. The open question is whether they make good decisions with it. We are building benchmarks that test exactly that, from designing a new binder from scratch to improving an existing one toward a measured function.
You will work across three connected areas:
De novo design. Given a target and a goal, can an agent design, filter and choose candidates that actually work? Tasks test the decisions that separate a real hit from a confident-looking failure: which design tool to trust, which filter to apply, which candidates to send to the lab.
Protein–protein interactions. Can an agent reason about a complex correctly? Tasks test whether it finds the true interface and its hotspots, tells a real interface from a crystal contact, reconciles co-folding models that disagree, and predicts how mutations change binding affinity and specificity.
Optimization. Given a de novo or redesigned protein, can an agent improve affinity, stability, expression or developability without breaking something else?
Every task is graded against real measured data: binder screens, binding affinities, deep mutational scans, combinatorial landscapes, developability panels and published engineering campaigns. Your job is to find the decision point in that data and turn it into a question with one verifiable answer.
The work sits under our biosecurity program. You will build tasks on therapeutics such as antibodies, nanobodies, minibinders and enzymes. You will also help test whether agents correctly decline to design or improve pathogen and toxin proteins.
What You'll Do
Build the task
Find measured design, binding and engineering data (binder screens, Kd titrations, SPR or BLI, DMS, combinatorial libraries, developability assays, directed-evolution lineages) and turn real design decisions into file-based tasks with automatic graders
Write gold-standard answers from raw data, with success defined against replicate noise and measured hit rates
Write Python pipelines that compute answer keys and check that an honest design route can reach them
Set the hard tasks
Build interface tasks: hotspot calls, interface versus crystal contact, specificity against off-targets, and affinity changes on mutation
Cover the full workflow: generate, filter, rank, combine, revert, triage pools and trade off competing objectives
Set floors (the tool alone, the best single metric, the screen's base hit rate) so an agent has to beat its own tools
Judge the agent
Read agent reasoning and code step by step, and find where a plausible design choice goes wrong
Reward calibrated decisions over confident ones
Requirements
PhD (or equivalent experience) in protein design, protein engineering, structural biology, biophysics, computational biology or a related field
Hands-on design or engineering in the last ~2 years, with experimental validation of your own designs: de novo binders or enzymes, antibody engineering, directed evolution, DMS, library design or ML-guided optimization
Working knowledge of protein–protein interfaces: hotspots, shape and charge complementarity, affinity and specificity, and how binding is measured (SPR, BLI, ITC, display screens)
Working knowledge of modern design and structure tools such as RFdiffusion, ProteinMPNN, BindCraft, AlphaFold, Boltz, Chai or ESM, and a clear sense of where each one fails
Deep understanding of at least one property: binding, stability, developability, enzyme activity or immunogenicity
You can explain why you chose a method, what the alternatives were and what would break it
Python for your own analysis, from raw screen, binding or design data to a conclusion
Daily use of AI tools, and the judgment to spot when a model's reasoning or code is scientifically wrong
Nice-to-Have
You have run a design campaign end to end: generation, filtering, expression and binding or activity assays
Antibody–antigen or nanobody work, or interface design for specificity
Multi-objective optimization or lab-in-the-loop design
RNA design
Experience building benchmarks, evals or grading logic
Compensation
$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 you learn our workflows.
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.
Benefits & Employment
In the US: full-time W-2
Guaranteed floor (“base salary”) set by your state, paid on the 15th and last day of every month
Health, dental, vision: 100% covered for you, 50% for dependents
401(k) (no match)
PTO: 15 days to start, more as you’re promoted
Outside the US: contractor
Paid on performance. We want our team to have stability, so we pay 60% of your monthly OTE up front as an advance, balanced against your earnings in later paychecks.
US benefits don’t apply.
Growth
Two tracks, based on output.
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 or keep growing on the IC track.
Location & Work Authorization
Location: remote or onsite at our Mission Bay office in San Francisco. Onsite is preferred: working side by side is how we share knowledge and keep our work rigorous. We have a global team, and remote teammates regularly visit the office.
Work authorization: we sponsor H-1B (new and transfers), O-1, and TN, and support STEM OPT.
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.
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).
Culture interview with our C-suite.
Offer.
Background check, once you accept.