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 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 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 RoleAs Product Manager, you will help turn Radical Numerics’ frontier AI research into products that scientists, biotech teams, governments, and biosecurity organizations can actually use. This is not a traditional SaaS PM role. You will operate at the intersection of AI research, biology, product design, and commercialization, helping define how general biological intelligence becomes a real platform for discovery, health, and defense.
You will work closely with the CEO, research team, engineering team, and external partners to identify the highest-leverage product opportunities, translate ambiguous scientific capabilities into clear user workflows, and drive execution from concept to launch. This may include products for biological design, statistical genetics, pathogen detection, multimodal biological analysis, or entirely new applications that emerge from our models.
You should be comfortable with ambiguity, technical complexity, and fast-moving priorities. One week you may be interviewing scientists and biotech users; another week you may be shaping a demo for strategic partners, writing product specs for a new model-powered workflow, or helping define the first version of a biosecurity platform. The ideal candidate is product-minded, technically fluent, user-obsessed, and motivated by the chance to help define a new category: AI systems that can understand, design, and defend biology.
Own product development for early Radical Numerics applications, from problem discovery through launch
Translate frontier AI capabilities into clear product concepts, workflows, and user experiences
Work closely with researchers and engineers to define product requirements, prioritize features, and drive execution
Conduct user research with scientists, biotech teams, biosecurity stakeholders, and strategic partners
Identify high-value use cases across health, biological design, statistical genetics, and biosecurity
Develop product specs, wireframes, demos, launch plans, and internal decision documents
Partner with leadership on product strategy, roadmap, positioning, and go-to-market priorities
Help turn ambiguous partner conversations into concrete product opportunities and requirements
Build feedback loops between users, commercial stakeholders, researchers, and engineers
Drive product quality, usability, and clarity in highly technical domains
Help shape the product culture of a frontier AI research lab
2–6 years of experience in product management, technical product, startup operations, or a highly analytical role
Strong product instincts: you can identify real user pain, simplify complexity, and turn vague ideas into concrete workflows
Technical fluency, especially in AI, software, data products, developer tools, computational biology, or scientific platforms
Excellent written communication; you can produce clear specs, product narratives, and decision documents
High ownership and bias toward action; you can operate without needing a fully defined roadmap
Comfort working in ambiguity, especially around emerging technology and novel markets
Ability to collaborate deeply with researchers, engineers, designers, commercial teams, and external partners
Strong judgment around product tradeoffs, user needs, and execution priorities
Low ego, high curiosity, and willingness to learn new scientific and technical domains quickly
Excitement about building at the frontier of AI, biology, and human health
Experience building AI, ML, developer tools, bioinformatics, biotech, or scientific software products
Background / exposure to biology, computational biology, genomics, biosecurity, drug discovery, or healthcare
Experience working with researchers or translating research into products
Experience at an early-stage startup or fast-moving technical organization
Familiarity with LLMs, foundation models, agents, sequence models, or multimodal AI systems
Experience designing products for technical users, scientists, or enterprise customers
Comfort with product analytics, user interviews, prototyping tools, or lightweight design workflows
Experience supporting demos, strategic partnerships, pilots, or enterprise customer discovery
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.
Skills Required
- 2-6 years of experience in product management, technical product, startup operations, or a highly analytical role
- Strong product instincts: identify user pain, simplify complexity, and produce concrete workflows
- Technical fluency in AI, software, data products, developer tools, computational biology, or scientific platforms
- Excellent written communication; produce clear specs, narratives, and decision documents
- High ownership and bias toward action; operate without a fully defined roadmap
- Comfort working in ambiguity around emerging technology and novel markets
- Ability to collaborate deeply with researchers, engineers, designers, commercial teams, and external partners
- Strong judgment around product tradeoffs, user needs, and execution priorities
- Low ego, high curiosity, and willingness to learn new scientific and technical domains quickly
- Excitement about building at the frontier of AI, biology, and human health
- Experience building AI, ML, developer tools, bioinformatics, biotech, or scientific software products
- Background or exposure to biology, computational biology, genomics, biosecurity, drug discovery, or healthcare
- Experience working with researchers or translating research into products
- Experience at an early-stage startup or fast-moving technical organization
- Familiarity with LLMs, foundation models, agents, sequence models, or multimodal AI systems
- Experience designing products for technical users, scientists, or enterprise customers
- Comfort with product analytics, user interviews, prototyping tools, or lightweight design workflows
- Experience supporting demos, strategic partnerships, pilots, or enterprise customer discovery
What We Do
The architectures that power modern AI were designed for language, not for science. They were never built to understand DNA, interpret experiments, or learn the rules that govern living systems. Closing this gap represents one of the defining opportunities of our time. Our team has pioneered advances across frontier AI, including the first models trained with one-million-token context windows, and now scaling toward one billion. We focus on innovations on systems and architecture, because building AI for science demands a fundamentally different foundation. As a first application of this technology engine, we are advancing AI for life, the most complex and consequential domain of all. We are the AI team behind Evo and Evo 2, the generative genomics models used to create real gene-editing tools and the first whole genomes designed entirely by AI. That work demonstrated that AI can create biology, not just analyze it. We are now building multimodal models trained directly on the fabric of biology, enabling faster discovery, deeper understanding, and entirely new capabilities. As we push the frontier of general biological intelligence, we will build systems hand-in-hand to ensure global biological resilience: rapid detection, rapid response, and rapid countermeasures against emerging threats, both natural and synthetic. Our mission is ambitious: to reimagine what AI can do for biology, and to build that future. Our advisors include Eric Horvitz, CSO of Microsoft, Chris Ré of Stanford, George Church of Harvard, and Andrew Weber, former Assistant Secretary of Defense for Nuclear, Chemical and Biological Defense Programs.
Why Work With Us
We bring the rigor of distributed systems, model architecture, and numerics research to the hard problems of scaling learning on biological data. If this resonates, we'd love to hear from you.
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