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 Role
You own the discovery side of our business. You identify where in pharma orgs our deals get funded, including discovery, data science, and therapeutic areas.
Our science team owns the model itself. You decide what we build on top of it for target discovery and validation. The people who evaluate us are genetics and computational biology groups, and they don't move without evidence, so you will have a lot of say in which benchmarks we run.
You should be comfortable building prototypes. You are paired with an applications engineer who builds production product, and you go into partner meetings together.
You will carry a commercial target from early on.
Decide which applications of the model matter for target identification and validation, and what we build first.
Originate and close partnerships with pharma discovery organizations. You are measured on committed contract value and on partners using the product week to week.
Find the internal champion, learn which budget funds a deal like ours, and give that champion what they need to get it approved.
Write the specs your engineer builds from, and stay with a partnership after signature
Tell our science team which benchmarks would move your deals, and make the case for running them.
Set pricing and structure with the Head of Product, and run the negotiation with our legal team.
Ensure key partners renew and expand.
Fluency in human genetics. GWAS, fine-mapping, burden testing, variant-to-function work. You can read a variant effect benchmark, tell whether the result means anything, and say so in front of a partner's chief scientist.
Breadth across the discovery workflow. You know what happens to a target after it is nominated, what evidence moves it forward, and what gets one killed.
You know how a large discovery organization buys. Which budget funds a platform deal, who else is competing for it, and where a collaboration dies internally.
You have sold to pharma R&D and carried a number. Deals you closed yourself, to scientific buyers.
You are comfortable as the only commercial person in a room of scientists, on both sides of the table.
You want to own revenue.
You have worked inside a pharma discovery organization.
You founded a company, or were one of the first ten people at one.
Published work in genetics, genomics, or computational biology.
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
- Fluency in human genetics (GWAS, fine-mapping, burden testing, variant-to-function)
- Breadth across the discovery workflow and understanding of target nomination, validation, and de-prioritization evidence
- Knowledge of how large discovery organizations buy, including which budgets fund platform deals
- Proven sales experience selling to pharma R&D and carrying quota; closed deals to scientific buyers
- Comfort building prototypes and writing technical product specs for engineers
- Ability to evaluate and recommend scientific benchmarks to move deals and present results to chief scientists
- Experience originating, negotiating pricing/structure, and closing partnerships, working with legal
- Comfort operating as the sole commercial person in rooms of scientists on both sides
- Desire and accountability to own revenue and drive renewals/expansions
- Experience working inside a pharma discovery organization
- Founder or early-stage employee experience
- Published work in genetics, genomics, or computational biology
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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