--- title: 'Member of Technical Staff, Infrastructure and Training Systems at Radical Numerics' canonical: 'https://feeny.ai/job/member-of-technical-staff-infrastructure-and-training-systems-radical-numerics-q27cz3p83trm' type: 'job' last_seen: '2026-09-07' --- # Member of Technical Staff, Infrastructure and Training Systems at Radical Numerics - **Company:** Radical Numerics - **Location:** San Francisco, CA - **Employment:** full-time - **Work type:** onsite - **Posted:** 2026-03-17 - **Last confirmed live:** 2026-09-07 - **Apply:** https://jobs.ashbyhq.com/radical-numerics/701179d1-dbf4-413c-827f-69853ac729f4 ## Job description ## ABOUT US Radical Numerics http://radicalnumerics.ai 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 https://www.science.org/doi/10.1126/science.ado9336, and presented by our CEO on the main stage of TED2025 https://www.youtube.com/watch?v=EnbfoFUFm2s. Evo was used to create the first AI gene therapy tool CRISPR-Cas9, and the first AI whole genome https://www.biorxiv.org/content/10.1101/2025.09.12.675911v1 from scratch. Evo 2 https://www.nature.com/articles/s41586-026-10176-5, featured in Nature, is the largest fully open source AI project across any domain. Radical Numerics is 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. 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, Infrastructure & Training Systems at Radical Numerics, you will design and build the systems that make large-scale model training possible across research and deployment workflows. You will work on distributed training, performance optimization, reusable internal frameworks, and the tooling that helps researchers move quickly without sacrificing reliability. This role is ideal for someone who combines deep systems instincts with an interest in modern machine learning. You should care about how every layer of the stack affects research velocity: kernel performance, communication overhead, fault tolerance, observability, reproducibility, and the ergonomics of the training loop itself. We believe biological world models will require not only strong research ideas, but exceptional training and inference systems: infrastructure that makes large-scale experimentation efficient, reproducible, and robust enough to support rapid scientific iteration. This role is focused on building that foundation. ## WHAT YOU’LL DO - Design and scale distributed training systems. Build and optimize distributed training infrastructure for large-scale biological world models across large distributed compute systems, with a focus on performance, stability, and scalability. - Maximize throughput and hardware efficiency. Develop performance optimizations across the stack, including communication patterns, memory efficiency, custom kernels, compilation paths, and systems instrumentation, to ensure training compute is used effectively. - Build reusable training frameworks. Develop internal libraries, abstractions, and workflows that improve reproducibility, reliability, and scalability across new model architectures and training recipes. - Improve reliability under rapid iteration. Establish standards and mechanisms for robustness, maintainability, debugging, and safe deployment of fast-moving research infrastructure. That includes fault tolerance, checkpointing, monitoring, experiment hygiene, and incident analysis. - Collaborate across research and engineering. Partner closely with model researchers, training scientists, and data/infrastructure engineers to identify bottlenecks, unblock experiments, and design systems that support new scientific directions rather than constrain them. - Support new architectures and training paradigms. Adapt infrastructure to the needs of multimodal models, long-context training, and evolving model architectures, so the systems stack remains a research multiplier as model requirements change. ## WHAT WE’RE LOOKING FOR - Strong engineering track record in distributed systems, high-performance ML infrastructure, training systems, or closely related areas. - Proficiency in building performant, maintainable software in Python, PyTorch, Triton, CUDA, and C++. - Strong understanding of modern deep learning frameworks and their systems internals. - Ability to debug complex, multi-layered systems involving distributed training, memory/performance regressions, and reliability issues in large codebases. - Comfort working in a highly collaborative environment with researchers, engineers, and domain experts, with a bias toward initiative and execution. - Excellent written and verbal communication skills bridging technical and scientific domains. ## NICE TO HAVE - Experience with large-scale distributed training for frontier or foundation models. - Contributions to open-source ML systems or infrastructure such as PyTorch, Torchtitan or Megatron-LM. - Familiarity with ML runtimes, compilers, numerics, communication libraries, and custom kernel development. - Experience improving researcher productivity through infrastructure design, developer tooling, or workflow improvements. - Background in applied math, systems, computational biology, or related quantitative sciences. ## WHY RADICAL NUMERICS - Help build the computational foundation for multimodal biological world models aimed at rapid detection, response, and countermeasures across global health. - Work on systems problems at the frontier of distributed training, architecture, and numerics, in service of real biological applications. - Join a collaborative culture that values rigor, creativity, and cross-disciplinary partnership across AI labs, biotechs, hospital systems, and research institutes. - Competitive compensation, comprehensive benefits, and support for continual learning. 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. Radical Numerics participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S. ## About Radical Numerics ## Company Overview - **One-liner**: Radical Numerics is a next-generation AI lab building general biological intelligence, developing multimodal models trained on the fabric of biology to enable discovery, design, and defense in the life sciences. - **Entity Type**: Private (Seed stage) - **Headquarters**: San Francisco, California, USA (with a presence in Tokyo, Japan) - **Founded**: Not publicly available (recently announced a $50M seed round) - **Founders**: Not publicly listed (the founding team previously trained Evo and Evo 2, the largest biological AI models trained on DNA) ## Core Business - **Primary industry**: Artificial Intelligence / Biotechnology (AI for Biology) - **Target customers**: B2B — partnerships with diagnostics companies, U.S. national labs, and pharmaceutical/therapeutic organizations; also serves the broader scientific community through open-source models. - **Mission or purpose statement**: "To master the code of life and to ensure this power is used to save lives." The company is building general biological intelligence to enable faster discovery, deeper understanding, and entirely new capabilities in biology, while simultaneously building biodefense systems to safeguard against misuse. ## Products & Services - **Evo & Evo 2**: The founding team's prior open-source generative genomics models, trained on millions of genomes across all of life. Evo was used to generate the world's first complete genome from scratch using AI (a bacteriophage). These models established the field of generative genomics. - **Omnii**: The company's next-generation genome language model (gLM). It features a multi-hybrid architecture with a 2M-token context window, multimodality, and is the first aligned large-scale genome model. It surpasses specialized models across core genetics benchmarks, especially in noncoding and regulatory regions. Omnii is being piloted for early cancer detection (with a diagnostics partner) and for biodefense/biosurveillance (with a U.S. national lab). - **Biodefense Platform (Omnii for Biosecurity)**: A system designed to detect "deepfake" pathogens, identify suspicious sequences, and attribute engineered function — aimed at global biological resilience against natural, synthetic, and AI-generated threats. ## Market Standing - **Valuation/Market Cap**: Not disclosed (private company) - **Key Metric**: $50 million seed round (announced in 2025) - **Notable Investors/Partners**: Not publicly named in the seed round announcement. Advisors include Eric Horvitz (CSO of Microsoft), Chris Ré (Stanford), George Church (Harvard), and Andrew Weber (former Assistant Secretary of Defense for Nuclear, Chemical and Biological Defense Programs). Partners include a U.S. national lab (for Omnii biodefense pilot) and a diagnostics company (for early cancer detection). - **Growth Signals**: Raised a $50M seed round; has a data center under construction filled with NVIDIA Blackwells; actively hiring for 11+ roles across San Francisco and Tokyo; building the next generation of biological AI models with a 2M-token context window; partnering with a U.S. national lab and a diagnostics company. ## Competitive Advantages - **Proven Track Record**: The founding team created Evo and Evo 2, the largest open-source biological AI models, which established the field of generative genomics. Evo generated the world's first complete AI-designed genome. - **First-Mover in Alignment for Biology**: Omnii is the first large-scale genome model to incorporate alignment methods (mid- and post-training), transforming a language model into a reliable scientific instrument. - **Dual Mandate (Design + Defense)**: Uniquely positioned as both a design lab and a defense lab under one roof, addressing the inseparable forces of biological advancement and biosecurity. - **World-Class Advisors**: Eric Horvitz (Microsoft CSO), Chris Ré (Stanford), George Church (Harvard), and Andrew Weber (former Assistant Secretary of Defense). - **Technical Depth**: Team has pioneered models with one-million-token context windows and is now scaling toward one billion, with a focus on novel architectures and systems engineering for scientific data. ## Strategic Focus - **Scaling Biological AI**: Building the infrastructure (data center with NVIDIA Blackwells) to train the most powerful biological AI models in the world. - **Dual Application**: Simultaneously advancing human health (cancer detection, therapeutic design, variant interpretation) and biodefense (pathogen detection, biosurveillance with U.S. national labs). - **Model Innovation**: Developing new multi-hybrid architectures, long-context windows (2M tokens and scaling to 1B), multimodality, and mechanistic interpretability tools for biology. - **Open Science Foundation**: While building proprietary systems, the team's prior work (Evo, Evo 2) was released fully open source, establishing credibility in the scientific community. ## Why Work Here - **Mission-Driven**: The company is tackling "the most important scientific and technological challenge of our time" — mastering the code of life to cure disease, engineer new medicines, and defend against biological threats. - **Cutting-Edge Technical Work**: Engineers and researchers work on frontier AI challenges including distributed systems, model architecture, numerics research, long-context models, and mechanistic interpretability — all applied to biological data. - **On-Site Culture**: The vast majority of roles (10 of 11 listed) are on-site in San Francisco, with some roles also available in Tokyo. This suggests a strong in-person collaboration culture. - **Interdisciplinary Team**: Brings together AI researchers, systems engineers, computational biologists, and scientists from institutions including Stanford, MIT, and Google DeepMind. - **High Ambition, Early Stage**: With a $50M seed round and a small team, new hires have the opportunity to shape the company's technical direction and infrastructure from the ground up. - **Dual Focus on Impact and Responsibility**: The company explicitly states that "the design lab and the defense lab need to be the same entity," offering engineers a chance to work on both creation and safeguarding of powerful technology. ## Sources 1. [radicalnumerics.ai](https://www.radicalnumerics.ai/) 2. [radicalnumerics.ai/about](https://www.radicalnumerics.ai/about) 3. [radicalnumerics.ai/blog](https://www.radicalnumerics.ai/blog/radical-numerics-seed) 4. [radicalnumerics.ai/join-us](https://www.radicalnumerics.ai/join-us) 5. 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