Member of Technical Staff (Statistical Genetics)

Location: San Francisco; U.S. work authorization required

Company

hirify.global is an AI research lab building general biological intelligence and generative genomics systems, including biological foundation models.

What you will 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 for genetic architecture, including linkage disequilibrium, ancestry, constraint, polygenicity, pleiotropy, regulatory effects, rare variant burden, and cross-population generalization.
• Partner with AI and machine learning engineers to analyze variant effect prediction, disease association, genotype-to-phenotype prediction, and regulatory region interpretation.
• Develop standards for genetic data provenance, quality control, leakage prevention, population bias assessment, privacy, consent, and responsible use of human genetic data.

Requirements

• PhD in statistical genetics, human genetics, computational biology, biostatistics, or a related field, or substantial industry experience with population-scale genetic data.
• Deep working knowledge of GWAS, linkage disequilibrium, ancestry and 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 with agentic coding tools.
• Clear communication skills for bridging scientific context with engineering teams and partner organizations.
• Curiosity and resilience when tackling open-ended scientific challenges.
• Must be authorized to work in the United States; the employer participates in E-Verify.

Nice to have

• Hands-on experience developing or benchmarking polygenic risk score methods.
• Familiarity with machine learning 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.
• Experience building reproducible pipelines, shared resources, open datasets, or benchmarking frameworks used by other scientists.

Culture & Benefits

• Work on foundational problems at the intersection of statistical genetics, machine learning, and large-scale biological data infrastructure.
• Collaborate across AI labs, biotechs, hospital systems, and research institutes.
• Competitive compensation and comprehensive benefits.
• Support for continual learning.
• Equal employment opportunity is provided across legally protected characteristics.