About Us

Radical Numerics is an AI research lab building general biological intelligence. Our mission is to master the code 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-generated 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.

In October, we introduced Omnii-1, the world's most advanced multimodal genome language model, and a leap toward general biological intelligence. It unifies 28 biological modalities, spanning DNA, RNA, proteins, epigenomics, and more, in a single model trained on trillions of tokens of biological data In a world first, Omnii-1 designed the first AI-generated bacteriophages to be administered to a human patient with an antibiotic-resistant infection under compassionate use. We released this news alongside seven other project areas, showing the model’s breadth of applications.

The same generative breakthroughs that enable life-saving cures also lowers 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, 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.