Member of Technical Staff, Statistical Genetics
Hybrid (SF Bay Area)Full-timePosted 5mo ago
About the role
Role focused on turning genetic association data into a rigorous substrate for biological foundation models, collaborating with AI engineers and computational biologists to design datasets, benchmarks, and analyses in statistical genetics.
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.
Qualifications
- 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.
Skills
Required:GWASLD and population structureQTL mappingvariant annotationdata provenance and privacycommunication
Last updated May 18, 2026. Interested? Apply on the company site.
About Radical Numerics
Next-gen AI lab for recursive self-improvement
Radical Numerics - Next-gen AI lab for recursive self-improvement
AIAI labrecursive self-improvement
More open roles at Radical Numerics
- Product Manager5mo agoHybrid (San Francisco, CA)
- Senior Engineering Manager5mo agoHybrid (SF Bay Area or Tokyo, Japan)
- Chief of Staff5mo agoHybrid (San Francisco Bay Area)
- Member of Technical Staff, Backend Engineering7mo agoHybrid (SF Bay Area), Hybrid (Tokyo, JP) · Engineering
- Head of Data Partnerships7mo agoHybrid (San Francisco Bay Area)