AI Biologist - Cancer Biology (Applications)

AI Biologist - Cancer Biology (Applications)

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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.

About the Role

Our work in Cancer Biology means measuring whether an agent can take a tumor case from raw molecular data to a real clinical decision. You'll identify real cancer-biology datasets and turn them into rigorous, deterministically-graded tasks.

Our benchmarks are agentic and cross-domain. Each task hands an agent the artifacts a cancer researcher would actually have and asks for a concrete conclusion. Your job is to establish defensible reference analyses and scoring criteria across conclusion types, the benchmark measures that span basic cancer biology to clinical outcomes. You'll anticipate where agents take plausible but wrong turns and test whether they can handle experimental variability, incomplete data, conflicting evidence, and mechanism-driven tradeoffs.

Our team emphasizes cross-domain integration and long-horizon reasoning.

Requirements

• 1+ years personally working in basic, translational, or clinical cancer biology. Experience with hands on analysis of multi-omic tumor data hands-on somatic/germline variant calling and interpretation (WES/WGS/panel; MAF/VCF), copy-number and structural-variant analysis, mutational signatures, bulk and single-cell RNA-seq (cell-state annotation, subtyping), tumor-immune contexture (immune deconvolution, spatial transcriptomics, multiplex imaging), subclonal reconstruction, or functional-genomics screens (CRISPR/RNAi), or clinical research is highly preferred.
• Proficiency in Python and/or R: VCF/MAF parsing, single-cell analysis (Scanpy/Seurat), gene-set scoring (GSEA/ssGSEA), immune deconvolution (CIBERS