Member of Technical Staff: Statistical Genetics

The role

You will turn genetic association data into a substrate for biological foundation models. You will build and evaluate large-scale statistical genetics resources and design benchmarks to test model performance on genetic architecture.

What you will do

• Build and evaluate large-scale statistical genetics resources for model training and assessment.
• Design benchmarks that test whether models capture genetic architecture.
• Partner with AI/ML engineers to analyze model behavior on variant effect prediction and disease association.
• Develop practical standards for genetic data provenance, QC, leakage prevention, and privacy.

Required 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 var
• 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.
• 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.

Benefits

• Competitive compensation
• Comprehensive benefits
• Support for continual learning

Before you apply

• Radical Numerics participates in E-Verify to confirm authorization to work in the U.S.

About the company

Radical Numerics is an AI research lab building general biological intelligence. Their mission is to master the code of life to reduce human suffering.