Member of Technical Staff, Statistical Genetics

Radical Numerics•San Francisco, CA

3d•Onsite

About The Position

As a Member of Technical Staff focused on statistical genetics, you will help us turn genetic association data into a rigorous substrate for biological foundation models. You will work with diverse data streams from GWAS, QTLs, variant annotation, biobank-scale phenotypes, and functional genomics to ask a central question: do our models understand the structure of genetic variation and its relationship to molecular and organismal traits? This role isКроме того, data architect, part methods scientist, and part model evaluator. You will collaborate closely with AI engineers and computational biologists to design datasets, benchmarks, and analyses that make models performant and scientifically interpretable.

Requirements

PhD in statistical genetics, human genetics, computational biology, biostatistics, or a related field, OR substantial industry experience working with population-scale genetic data.
Deep working knowledge of concepts and methods in statistical genetics: GWAS, LD, ancestry/population structure, heritability, fine-mapping, QTL mapping, rare variant analysis, polygenic risk, and variant annotation.
Experience with large genetic resources such as UK Biobank, All of Us, TOPMed, gnomAD, GTEx, FinnGen, ENCODE, or similar datasets.
Strong computational fluency with Python, HPC, and modern genomic data tooling.
Clear communicator who can bridge scientific context with engineering teams and partner organizations.
Curiosity and resilience when tackling open-ended scientific challenges.

Nice To Haves

Experience integrating genetics with functional genomics, single-cell data, perturbational screens, proteomics, metabolomics, imaging, or clinical phenotypes.
Familiarity with ML for genomics, including sequence models, variant effect predictors, regulatory models, multimodal models, or biological foundation models.
Experience with colocalization, Mendelian randomization, TWAS, causal inference, cross-ancestry genetics, admixed populations, or privacy-preserving genomic analysis.
A track record of building reproducible pipelines, shared resources, open datasets, or benchmarking frameworks used by other scientists.

Responsibilities

Build and evaluate large-scale statistical genetics resources for model training and assessment, including GWAS summary statistics, QTL maps, fine-mapping results, variant annotations, haplotypes, population reference panels, and biobank-scale phenotype data.
Design benchmarks that test whether models capture genetic architecture: linkage disequilibrium, ancestry, constraint, polygenicity, pleiotropy, regulatory effects, rare variant burden, and cross-population generalization.
Partner with AI/ML engineers to analyze model behavior on variant effect prediction, disease association, genotype-to-phenotype prediction, regulatory region interpretation.
Develop practical standards for genetic data provenance, QC, leakage prevention, population bias assessment, privacy, consent, and responsible use of human genetic data.