AI Neuroscientist
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 Neuroscientist to join our Neuroscience team, pushing what AI can achieve in neuroscience.
About the Role. You will be the scientific authority on ground truth, testing whether agents can reason like an expert neuroscientist. AI agents today struggle to interrogate raw evidence across molecular, cellular, circuit, tissue, and behavioral levels, separate real effects from artifacts and confounds, and reach conclusions and R&D decisions an expert would stand behind. You will work with software engineers and neuroscientists to build the tasks and datasets behind our benchmarks across normal nervous-system function and disease; your judgment will set the standard agent outputs are held to, including:
Build the task
Define what expert reasoning looks like for a research question or R&D decision, and write gold-standard answers with the rationale an experienced neuroscientist would defend
Design realistic research scenarios across the workflow, from hypothesis and target selection through model-system choice, experimental design, data processing, and inference
Curate multi-modal data bundles (electrophysiology, calcium imaging, EEG/sEEG, MRI/PET, single-cell and spatial transcriptomics, histopathology, behavior, biofluid markers)
Pair basic-science and disease tasks (e.g., hippocampal replay in health and its disruption in epilepsy or Alzheimer’s disease) to test whether an understanding of normal function transfers to pathology
Partner with software engineers to turn your judgment into new workflows
Set the hard calls
Test whether agents choose the right model system and unit of analysis, and recognize when rodent, iPSC, or organoid findings will not translate to human disease
Adjudicate contested claims in the literature: decide what the data supports, what they do not, and which experiment would settle the question
Judge the agent
Interrogate agent reasoning step by step, and challenge conclusions that are fluent and textbook-correct but would not survive lab meeting, peer review, or a go/no-go decision
Pinpoint where agents go wrong: state 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 binary calls when evidence is incomplete
Requirements
PhD or MD/PhD in neuroscience or a closely related field
Hands-on neuroscience research experience with first-hand data analysis and a publication record
Direct experience analyzing raw data in at least two modalities: electrophysiology (patch-clamp, MEA, Neuropixels), optical imaging, EEG/sEEG, neuroimaging, single-cell or spatial omics, neuropathology, or quantitative behavior
Deep expertise in at least one area (systems/circuit, cellular/molecular, clinical/translational, or computational neuroscience) and working fluency across system levels
Strong command of experimental design and statistics
Proficiency in AI agent use, with the judgment to identify 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
Practical understanding of model systems (iPSC neurons, organoids, rodents, NHP, human cohorts)
Familiarity with core resources is a plus: Allen Brain Cell Atlas/BICAN, DANDI/NWB, OpenNeuro/BIDS, IBL, ADNI, PPMI, UK Biobank, ClinVar/gnomAD
Culture. We practice radical transparency: you’ll know how the company is doing, what’s working, and what isn’t. It’ll always be us against the problem, never each other. We’re opinionated but respectful, and we bring solutions with our problems. You decide how you work; we judge the output. We use AI constantly, so what we reward is 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. People who do well here and bring leadership experience can move quickly into senior roles. Outside of work, we have company outings most months, team off-sites, a Latch run club, cycling, boxing, ice skating, and soccer. Feel free to add to the list.
Compensation & Logistics
Contract: 1099 (or W-8BEN internationally), no end date
Pay: guaranteed for weeks 1-4 while you ramp, then paid per certified task. Most people in this position earn $120K–$180K+ (uncapped upside); certify twice the tasks/week, earn twice the pay that week.
Commitment: full-time, on your own schedule. Most of the team works 40+ hours a week.
Meetings: a few pod check-ins a week. The rest of your time is yours.
Location: remote (anywhere), hybrid, or onsite in SF. We prefer onsite.
Onsite perks: catered lunch and dinner daily, a waterfront office in Mission Bay, and monthly parking when available
Work authorization: US citizens, permanent residents, and OPT holders. We can’t support STEM OPT extensions or sponsorship.
Interview Process
We move fast: about 2–3 weeks from your first call, to offer.
Intro Screen - Latch Talent Team
Take-Home Project - HackerRank
Technical Interview - Member of Technical Staff
Culture Interview - C-Suite
Offer
As a Research Staff member, you will develop innovative Latent Space Models to address the challenges of building robust voice AI, focusing on advanced audio codecs and generative models.
Develop Latent Space Models and foundational audio AI methods, from neural codecs and generative speech systems to scalable data pipelines and hardware-efficient architectures for real-time voice interaction.
As a Research Staff member, you will develop innovative Latent Space Models to address the challenges of building robust voice AI, focusing on audio codecs, generative models, and scalable training techniques.
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Submit an expression of interest for future research scientist or research engineer opportunities, sharing your background, research interests, and questions you would like to explore in frontier AI safety.
