About this role
AI Biologist - Microscopy
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 Microscopy team, pushing what AI can achieve in biological imaging.
About the Role. You will be the scientific authority on ground truth, testing whether agents can reason like an expert microscopist. AI agents today struggle to interrogate raw images, separate real biology from artifacts in acquisition, staining, and segmentation, and reach conclusions an expert would stand behind.
Experts themselves disagree on roughly 1 in 6 images, so knowing where the evidence ends is part of the work. You will work with software engineers and scientists to build the tasks and datasets behind our benchmarks across cell biology, tissue, and disease. Your judgment will set the standard agent outputs are held to, including:
Build the task
Define what expert reasoning looks like for an imaging question, and write gold-standard answers with the rationale an experienced microscopist or pathologist would defend
Design realistic scenarios across the imaging workflow: sample prep, staining, acquisition, preprocessing, segmentation, quantification, and interpretation
Curate image datasets across modalities: immunofluorescence, confocal and live-cell, electron microscopy, histology and histopathology, and high-content screening
Build analysis pipelines in Python, including deep-learning segmentation, that produce verifiable ground truth
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 bleed-through, uneven illumination, fixation damage, out-of-focus planes, or segmentation failure
Adjudicate ambiguous images: decide what the data supports, where reasonable experts would disagree, and which additional stain, image, or experiment would settle the call
Test whether agents choose the right imaging modality, resolution, and unit of analysis for the question
Judge the agent
Interrogate agent reasoning step by step, and challenge conclusions that sound right but would not survive pathologist 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 images
Requirements
PhD in cell biology, bioimage analysis, pathology, or a related field; or an MD with pathology training
Senior, hands-on imaging experience: you have looked at thousands of samples and know what normal and abnormal look like
Direct experience with image segmentation tools such as CellProfiler, Cellpose, StarDist, QuPath, ilastik, napari, or Fiji
Deep expertise in at least one area (bioimage analysis, cell biology, or histopathology) and working fluency across the others
Strong command of experimental design, image quantification, 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: electron microscopy, pathology, or pharmacology experience (e.g., pharmacodynamic or drug-uptake imaging assays)
Bonus: familiarity with public resources such as the Image Data Resource (IDR), BioImage Archive, Human Protein Atlas, Cell Painting Gallery, or TCGA whole-slide images
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.