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.
About 83 Sciences
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 Most experimental data (failed runs, unpublished results, raw instrument output) never gets captured.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.
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 you'll do
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 process conditions
Work directly with research partners; watch scientists use what you built, then improve it
Help shape model architecture, data pipelines, and technical direction as an early team member
Build the data engine that turns messy experimental data (synthesis notes, PXRD, characterization) into training-grade datasets
What we're looking for
1+ years of experience managing technical projects at startups and team members or big companies / research labs (e.g., FAIR Chemistry, Google DeepMind, Microsoft Research, OpenAI, Anthropic Lila Sciences, MIT/Stanford/Berkeley/CMU/UToronto AI-for-science groups, or similar)
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
Logistics:
NYC in-person (negotiable for the right person). US work authorization required.
Nice to have:
model architecture/fine-tuning experience; background in chemistry, materials science, or scientific tooling; familiarity with scientific data (spectra, diffraction patterns, instrument output), A first-author or major-contributor paper in ML-for-science, geometric ML, molecular/crystal generation, scientific agents, interatomic potentials, synthesis planning, or active learning.