Member of Technical Staff, Computational Biology

Location: SF Bay Area
Type: Full-time

About Radical Numerics

Radical Numerics is an AI lab bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We are building the infrastructure needed to unlock scaling on vast multimodal biological datasets so that biological world models become a reality. Our team introduced the first hybrid architectures that unlocked million-token context windows, enabling the first AI-designed whole genomes and real gene-editing tools.

About the Role

As a science-focused Member of Technical Staff, you will curate the multimodal biological datasets that power our models, analyze model behavior, and ensure our model outputs meet rigorous scientific standards. You'll co-develop benchmarks, filters, and validation pipelines with engineering peers so biological world models remain trustworthy and actionable.

What You'll Do

• Source, normalize, and steward large-scale genomic, epigenomic, transcriptomic, proteomic, and imaging datasets with rigorous metadata and provenance.

• Build evaluation suites and benchmarks that stress-test generative biological models across modalities and tasks.

• Partner with AI engineers to analyze model outputs, run ablations, and surface insights that guide future architecture and training improvements.

• Integrate new datasets and annotations from external collaborators while maintaining compliance, privacy, and ethical standards.

• Communicate findings and best practices across Radical Numerics so teams can trust and act on model results.

What We're Looking For

• PhD in genetics, computational biology, or a related field, OR demonstrated experience in biotech with a strong track record of impact over 3+ years.

• Proven experience curating, harmonizing, and analyzing large biological datasets (e.g., genomics, single-cell, spatial, or imaging).

• Fluency with Python, data tooling, and reproducible workflows (git, notebooks, containers).

• Ability to interrogate model outputs, debug unexpected behaviors, and translate findings into actionable recommendations.

• 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

• Familiarity with generative model evaluation, red-teaming, or safety analysis in scientific domains.

• Experience with statistical validation, quality control, or benchmarking for scientific or ML systems.

• Experience building benchmarking frameworks or open datasets that became community standards.

• Contributions to shared analytics tooling or reproducible research pipelines.

Why Radical Numerics

• Help produce the multimodal biological world models that will power rapid detection, response, and countermeasures across global health.

• Collaborative culture that values rigor, creativity, and cross-disciplinary partnership across AI labs, biotechs, hospital systems, and national research institutes.

• Competitive compensation, comprehensive benefits, and support for continual learning.

How to Apply

Send your resume, a brief note on why Radical Numerics resonates with you, and examples of relevant public codebases you’ve built. We review applications on a rolling basis.

Radical Numerics is committed to equal employment opportunity and does not discriminate in any employment opportunities or practices based on an individual's race, color, creed, gender (including gender identity and gender expression), religion (all aspects of religious beliefs, observance or practice, including religious dress or grooming practices), marital status, registered domestic partner status, age, national origin or ancestry (including language use restrictions and possession of a driver’s license issued under California Vehicle Code section 12801.9), natural hair, physical or mental disability, political affiliation, medical condition (including cancer or a record or history of cancer, and genetic characteristics), sex (including pregnancy, childbirth, breastfeeding or related medical condition), genetic information, sexual orientation, military and veteran status or any other consideration made unlawful by federal, state, or local laws. It also prohibits unlawful discrimination based on the perception that anyone has any of those characteristics, or is associated with a person who has or is perceived as having any of those characteristics.