Member of Technical Staff, Science

Location: SF Bay Area or Tokyo, Japan
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.