Member of Technical Staff, Epigenetics / Therapeutics / Single-Cell

LatchBio is building AI agents that do real biology. Agents that can take raw experimental data and return a defensible scientific claim. The hard part is defining what good analysis actually looks like across the assays and decisions that matter in real R&D, and building benchmarks that can tell when an agent is doing it right.

In this role, you will:

• Translate the tacit workflow knowledge in your head into structured, reproducible analysis patterns with clear ground truth and known failure modes

• Review agent outputs and engineer-built pipelines. Catch what looks right but isn't. Decide what an acceptable analysis looks like

• Work directly with our CTO, head of product, and a small bioinformatics team to define new approaches.

Three tracks. Apply to the track where you have the most depth and note your primary track in your application. If you span two domains, flag the secondary.

Epigenetics track:

• Personal end-to-end analysis of ATAC-seq, ChIP-seq, CUT&RUN/CUT&Tag, scATAC-seq, multiome, or Hi-C

• You can explain why a peak opens, not just that it did

• Fluent in tools like MACS, ArchR, Signac, ChromVAR, deepTools

Therapeutics track:

• Personal end-to-end analysis of preclinical drug discovery data: HTS plates, dose-response curves, CRISPR screens, PRISM/DepMap/GDSC response matrices, ADMET panels, PK/DMPK

• You know the difference between a real selective hit and broad cytotoxicity, and between a clean IC50 and a curve that shouldn't be reported

• Fluent in tools like scipy curve fitting, MAGeCK, RDKit

Single-cell track:

• Personal end-to-end analysis of scRNA, snRNA, CITE-seq, spatial transcriptomics (Xenium, Visium, MERFISH, Stereo-seq), or Perturb-seq

• You have intuition for kit-specific QC thresholds (you know 100K cells from a 10X run means something is wrong)

• Fluent in tools like Scanpy, Seurat, Cell Ranger, scVI, Squidpy

You must have:

• 3-5+ years of hands-on primary analysis experience in one of the three tracks

• Analyzed 3+ datasets in your domain with real consequences (publication, product decision, clinical or regulatory submission)

• Built or contributed to open-source tools, internal platforms, or production pipelines used by people other than you

What we don't want:

• People who supervised analyses but didn't do them

• Wet-lab scientists for whom the assay was a means to an end

• People who can describe a workflow but can't write the grading script for it

Compensation and logistics:

• $200k+ base, increasing with track, depth, and experience

• Significant equity

• 100% premium covered Blue Shield platinum health plan ($0/$0)

• Free lunch and dinner

• Unlimited PTO

• Waterfront office in China Basin, San Francisco

• Five days per week in person, non-negotiable

• Visa sponsorship available

Hiring process, typically one week start to offer:

• Apply with resume and a few sentences on track and why

• Intro call with Saul (Technical Recruiter) or Kyle (COO)

• Take-home designed by track

• Technical with Kenny (CTO)

• Offer

If you've ever wanted an AI that understands the data you've spent your career analyzing, come build it.