
AI-native materials discovery powered by unpublished experimental data
83 Sciences is building the data and AI infrastructure for scientific discovery. Almost 90% of experimental data is discarded, losing over $100B in R&D value every year. 83 Sciences turns unused experimental data into the materials and manufacturing processes powering the next industrial revolution.
Our ambition is to own or license the world’s experimental data and use it to build better models for science.
Your first job is to build our university data network, starting with individual labs and scaling to departments and institution-wide partnerships. From there, you’ll expand into industry R&D and other major sources of proprietary experimental data.
You will:
We’re looking for someone who can operate like a founder: build relationships from scratch, manage ambiguity, move quickly, and turn partner insight directly into product and strategy.
Your mandate: build the network that gives 83 Sciences access to the world’s most valuable experimental data.
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.
90% of experiments never make it to publication. Failed runs, abandoned hypotheses, and routine characterization data live in scientists' heads and scattered notebooks. 83 Sciences captures that hidden data at the source, structures it into a queryable "lab brain," and puts it to work: helping researchers learn from their lab's full history and discover new materials.
We are working hands-on with our first cohort of university lab partners and we're backed by Y Combinator (S26). Founded by scientists who lived the file drawer problem firsthand — we're a small team where everyone ships product and talks to researchers directly.