Founding AI Engineer

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 Most experimental data (failed runs, unpublished results, raw instrument output) never gets captured.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 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

• 1+ years of experience managing technical projects at startups and team members or big companies / research labs (e.g., FAIR Chemistry, Google Deep Mind, 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

NYC in-person (negotiable for the right person). US work authorization required.

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.