AI Neuroscientist

LatchBio•San Francisco, CA

10d•$120,000 - $180,000•Remote

About The Position

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)

Nice To Haves

Familiarity with core resources is a plus: Allen Brain Cell Atlas/BICAN, DANDI/NWB, OpenNeuro/BIDS, IBL, ADNI, PPMI, UK Biobank, ClinVar/gnomAD

Responsibilities

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