Bioinformatics Engineer — Single-Cell AI

About the position

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.

Responsibilities
• 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
• 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 for core workflows, cell typing frameworks like CellTypist or Azimuth, DE methods like DESeq2 or edgeR)
• Strong understanding of experimental design, hypothesis generation and scientific conclusions from papers using one of the sequencing platforms described
• Ability to distill an analysis step into a precise, falsifiable biological question with a single defensible answer
• Working understanding of concepts in statistical inference: hypothesis testing, confidence intervals and/or estimators
• Working understanding of important algorithms in high dimensional data analysis: e.g. PCA, neighborhood graphs, UMAP, clustering methods (Leiden/Louvain)

Nice-to-haves
• 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.

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

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