Founding AI Research CTO

83 Sciences is looking for its 4th co-founder and CTO — clear first choice over a founding engineer. This person takes the CTO seat and owns the AI/ML engineering and the entire data-and-discovery engine, freeing current CTO Eric — who is very strong on the science — to move into a chief-science / application-focused role. Because a several-million seed is closing shortly and the plan is to hire engineering talent fast after it, the co-founder mandate explicitly includes recruiting and building the team, so a strong network in AI-for-materials is a real asset. For a genuine rockstar who isn't ready for co-founder risk, there's a founding-engineer path that can grow into CTO.

Concretely, the first ~6 months: Month 1 — build high-throughput, standardized data-ingestion pipelines to pull in 20+ labs' unstructured experimental data. Months 2–3 — demonstrate model-performance uplift: benchmark models with vs. without the experimental data to produce hard proof points. Months 4–6 — run the materials-discovery pipeline end to end to surface novel, commercially relevant materials the founders take to industry, while hiring out the team below them. It's hands-on infrastructure and modeling first, paired with the team-building and partnership judgment of a co-founder.

What You'll Own

• The CTO seat: technical direction for the AI/ML engineering and the data-and-discovery engine
• Data-ingestion pipelines — a high-throughput, standardized way to pull 20+ labs' unstructured experimental data into structured form
• Model-performance uplift: benchmarks proving how much the experimental data improves the models
• Running the materials-discovery pipeline to surface novel, synthesizable, commercially relevant materials
• Recruiting and building the engineering team as the seed closes
• Backend infrastructure that lets CTO Eric shift into a science/application-focused role

Requirements

• Has built, trained, and deployed AI-for-materials models — the hard requirement; AI-for-bio (AlphaFold-type) does not count, materials is a distinct skill set
• Can personally build and train a model end to end: collect and clean the data, structure the architecture, and show measured performance uplift
• Strong on data infrastructure / pipelines, not just research
• Leverages frontier models, not legacy ML-for-materials approaches
• Co-founder-caliber: geared to recruit, manage, and build a team, not just execute
• High-ownership, fast-moving, hands-on builder comfortable at a pre-seed YC startup
• Team player with a "front of the jersey" ethos — when something breaks it's everyone's fault; supportive over critical
• Sense of humor and a life outside work; not the intense all-work YC archetype

Background

• Later-career, not straight out of college; real industry experience
• High intellectual horsepower — top university, then a Google DeepMind or Meta FAIR AI-for-science team, or a PhD/postdoc with a commercial bent, or another AI-for-materials startup (Radical AI, Orbital Materials, Lila Sciences, Periodic Labs)
• Bonus: a large network in AI-for-materials to recruit from; outside-the-box, commercially curious thinking

Nice-to-Have

• A strong recruiting network in AI-for-materials
• Commercial creativity — thinking beyond the pure technical domain
• (Fallback path) a founding engineer clearly on a trajectory to CTO

Who Will Thrive Here

• An AI-for-materials leader who wants a real co-founder seat — 20% equity, the CTO title, and the mandate to build the team
• Someone excited by ownership and speed a big lab can't offer — hands-on with 15+ professors' data, not one, in a much faster-paced environment
• A supportive team player with a sense of humor and a life outside work
• Later-career, industry-seasoned, high-horsepower, frontier-model-native
• Motivated by access to a uniquely broad set of real-world experimental data