At LatchBio, our AI agents help thousands of scientists analyze and interpret data across the full stack of modern multi-omic technologies — starting with single-cell and spatial, and expanding fast.

We're building the ground truth for AI in single-cell biology. Our benchmark scBench — 394 verifiable problems across six sequencing platforms — shows the best frontier model today still fails nearly half the time. We're hiring bioinformatics engineers to close that gap: scientists who can turn real experimental data into the precise, falsifiable questions that define what it means for an AI agent to actually understand scRNA-seq.

What you'll do

Own end-to-end scRNA-seq analyses across multiple projects: raw platform outputs → QC and failure diagnosis → normalization → dimensionality reduction → clustering → cell typing → differential expression → trajectory analysis → defended biological claim.
Build reproducible workflows and produce clear decision traces: what was filtered, why, what changed the conclusion, what would falsify the claim.
Distill analysis steps into precise, falsifiable biological questions with single defensible answers — the core unit of our eval suite.
Debug platform and data issues with precision: turn messy results across diverse sequencing chemistries into crisp hypotheses, sanity checks, and a stepwise debugging plan.

Requirements (must-have)

Experience with end-to-end data analysis for one or more of the following sequencing platforms: MissionBio, ParseBio, CSGenetics, BD Rhapsody, Illumina, or 10X Chromium
- Analyzed 3+ datasets from raw data to end insight for either publications or industry experiments with real world consequences
- Working understanding of platform-specific quality control thresholds and intuition for numerical examples of positive or negative results (e.g., 100K cells from a ParseBio run with 80% mitochondrial reads means something is wrong)
- Familiarity with the landscape of computational biology tools for scRNA-seq tasks (e.g., Scanpy/Seurat, CellTypist, DESeq2)
Strong understanding of experimental design and hypothesis generation — can read a paper, extract the key biological result, and formalize it as a precise, falsifiable question
Working understanding of statistical inference and high-dimensional data analysis: hypothesis testing, PCA, neighborhood graphs, UMAP, clustering (Leiden/Louvain)

Desired experience (nice-to-have)

Published research that relied on modern single-cell RNA sequencing techniques.
Engineered tools or packages in the single-cell biology domain.
Experience generating training data for AI agents or foundation models.

Ideal candidate

You are a scientifically fluent engineer who has run real scRNA-seq pipelines and knows where they break. You can look at a clustering result and form a biological opinion about it — not just report what the algorithm returned. You're comfortable being wrong, updating beliefs with evidence, and writing down decisions so others can reproduce and critique your work. You think in falsifiable questions and know the difference between a result that's numerically correct and one that's biologically meaningful.

Compensation & benefits

$130k–$180k/yr (performance-based)
Equity
Unlimited PTO (truly)
Waterfront office in China Basin, San Francisco
Free lunch and dinner
100% premium covered on Blue Shield's platinum health plan ($0 premium, $0 deductible)
401(k) plan options
Work visa sponsorship
Company-sponsored professional development

Full-time preferred, part-time available.
In-person in San Francisco preferred, remote options available.

About the team

We work on serious problems at the most important intersection in history: biology and AI. We are building a team of world-class people, and are all eager to dedicate a substantial part of our life to solving these problems.

If we succeed we will hugely accelerate scientific progress and aid the creation of therapies for cancer, solutions to global warming, and cures for aging.

Who you'll work with

Spatial Biology: Zach, Alex, Ben, & others.
Sr. Bioinformatics Engineers: Zhen, Harihara
Product: Hannah and Nathan
Founders & Chief of Staff: Alfredo, Kyle, Kenny, Jordan

How to apply

Apply on our Ashby posting here.

Hiring process

Our process moves quickly, typically completed within one week.

Round 0: Apply with a resume and cover letter
Round 1: Introduction — Saul (Technical Recruiter)
Round 2: Take-Home Project — Zhen and Harihara (Bioinformatics)
Round 3: Technical — Kenny (CTO)
Round 4: Culture — Jordan (Chief of Staff) or Kyle (COO)
Offer