AI Biologist - Microscopy

LatchBio; Oregon; Hybrid; USD 140,000 - 180,000; Full time

Job summary

LatchBio is seeking an AI Biologist to lead ground-truth development and imaging workflow design for microscopy benchmarks. You will craft gold-standard answers, curate diverse imaging datasets, and build Python-based analysis pipelines with deep learning components.

You will work with software engineers to translate judgment into scalable workflows. The role emphasizes expert reasoning, interpretation across modalities, and maintaining scientific rigor in AI-driven image analysis.

Qualifications

• PhD or MD with pathology training as stated in the requirements.
• Senior hands-on imaging experience with biological samples.
• Direct experience with image segmentation tools.

Responsibilities

• Define expert-like ground truth and gold-standard answers with rationale.
• Design realistic imaging workflows across sample prep, staining, acquisition, and analysis.
• Curate datasets across modalities including immunofluorescence, confocal, histology, and high-content screening.
• Build Python analysis pipelines, including deep-learning segmentation, to produce verifiable ground truth.
• Collaborate with software engineers to implement new workflows.

Skills

Imaging experience

Bioimage analysis

Statistics

Experiment design

Uncertainty communication

Education

PhD in cell biology, bioimage analysis, pathology, or related field

MD with pathology training

Tools

CellProfiler

Cellpose

StarDist

QuPath

ilastik

napari

Fiji

Job description

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.

• 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-2Guaranteed 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: contractorPaid 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

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.