PhD in statistical genetics, human genetics, computational biology, biostatistics, or related field, or substantial industry experience with population-scale genetic data.
Deep knowledge of statistical genetics concepts and methods, including GWAS, LD, and polygenic risk.
Experience with large genetic datasets like UK Biobank or gnomAD.
Strong programming skills in Python or R for scientific analyses.
Effective communicator bridging scientific and engineering teams.
Curiosity and resilience in addressing complex scientific challenges.
Responsibilities
Build and evaluate large-scale statistical genetics resources for model training and assessment.
Design benchmarks to test models on genetic architecture capture.
Collaborate with AI/ML engineers to analyze model behavior on genetic predictions.
Develop standards for genetic data provenance, quality control, and responsible use.
Benefits
Opportunity to contribute to groundbreaking biological models of human genetic variation.
Engagement with foundational problems in statistical genetics and machine learning.
Collaborative environment with diverse teams across AI labs, biotechs, and research institutes.
Support for continual learning and professional development.
Full Job Description
About the Role
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 is part 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. What You'll Do
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, and 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.
What We're Looking For
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 Python or R programming skills and experience building reliable, reproducible scientific analyses or software. Experience working with agentic coding tools.
Clear communicator who can bridge scientific context with engineering teams and partner organizations.
Curiosity and resilience when tackling open-ended scientific challenges.
Nice to Have
Hands-on experience developing or benchmarking polygenic risk score methods.
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.
Why Radical Numerics
Help build biological world models that understand human genetic variation, not just reference sequences.
Work on foundational problems at the intersection of statistical genetics, machine learning, and large-scale biological data infrastructure.
Join a collaborative team spanning AI labs, biotechs, hospital systems, and research institutes, with competitive compensation, comprehensive benefits, and support for continual learning.
