Member of Technical Staff, Statistical Genetics

Member of Technical Staff, Statistical Genetics Location: SF Bay Area Type: Full-time About Radical Numerics Radical Numerics is an AI research lab building general biological intelligence. Our mission is to master the language of life, and our purpose is to reduce human suffering. Our team created Evo, and started the field of generative genomics . Our work was featured on the cover of Science, and presented by our CEO on the main stage of TED2025. Evo was used to create the first AI gene therapy tool CRISPR-Cas9, and the first AI whole genome from scratch. Evo 2, featured in Nature, is the largest fully open source AI project across any domain. Radical Numerics is an AI lab bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We’ve redesigned the foundation model training stack to turn the world’s raw scientific data (e.g. biological sequences, experiments, and physical processes), into intelligible, generative models that can expand and accelerate what humanity can understand, design, and cure. We are building a new type of AI research lab, driving both the frontier of biological AI for human health, and building the systems to defend against its potential misuse. The same generative breakthroughs that enable life‑saving cures also lower the barrier to creating engineered threats and AI‑generated bioweapons. We believe these forces are inseparable. Radical Numerics was founded to develop both the power to design and the responsibility to defend. 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, 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 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 Have 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. 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. #J-18808-Ljbffr Radical Numerics