AI-native materials discovery powered by unpublished experimental data. Backed by Y Combinator.
About the role
We're hiring a founding AI engineer to help design insight and discovery extraction models and ship tools for scientists. You’ll own the architecture and build the ML stack alongside our Chief Science Officer and work directly with the founders.
What they're looking for
- Deep in at least two of: geometric deep learning (E(3)/SE(3)-equivariant GNNs), generative models (diffusion, flow matching), ML interatomic potentials
- A track record of shipping enterprise-ready products to real users, end to end, with FDE / customer facing technical experience a plus
- Comfort across the stack: you can get a feature all the way out the door & bias toward speed and ownership in a small, fast-moving team
More about this role
We use AI to mine discarded experimental data and drive scientific breakthroughs. Working alongside labs, we discover the materials that will power the new Industrial Revolution.
83 Sciences (YC S26) is the intelligence engine powering the future of research and materials discovery. Most experimental data (failed runs, unpublished results, raw instrument output) never gets captured. We turn raw lab signals into novel discoveries: capturing and structuring experimental data, shortening research processes, and surfacing the insights that drive new materials.
We're hiring a founding AI engineer to help design insight and discovery extraction models and ship tools for scientists. You’ll own the architecture and build the ML stack alongside our Chief Science Officer and work directly with the founders.
- Ship enterprise-ready products across our platform: data capture, structured experimental records, querying, and analysis tools
- Build our AI systems: multimodal pipelines (vision models for handwritten notebook pages and drawn structures, speech-to-text at the bench), agents that reason over a lab's full experimental history, and models that predict outcomes and propose optimized...
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