83 Sciences
AI-native materials discovery powered by unpublished experimental data
Full-Stack Software Engineer
About the role
We use AI to mine discarded experimental data and drive scientific breakthroughs. Working alongside labs, the goal is to collect the world’s experimental data into one platform.
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 Most experimental data (failed runs, unpublished results, raw instrument output) never gets captured.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 full-stack engineer to build our next-generation electronic lab notebook and research platform. You'll work directly with the founders to design and ship products from the ground up. This is a high-ownership product engineering role (distinct from our Founding AI Engineer role, which owns the ML stack).
What you'll do
- Build features across the entire stack: experiment planning, laboratory inventory management, and integrated data analysis with Jupyter notebooks
- Ship AI-powered workflows, like digitizing handwritten lab notebooks into structured experimental records
- Turn ambitious ideas into polished, reliable software. Then, watch scientists use what you built and improve it
- As the platform grows, tackle advanced capabilities: spectroscopy analysis, intelligent search, and chemistry-specific tools
What we're looking for
- 3+ years of professional experience shipping production web applications end to end
- Strong with React/Next.js, TypeScript, Python, PostgreSQL, and modern cloud infrastructure
- You’ve worked at an AI for Science company, Tech company, or Tech startup where you have built products from scratch, worked independently, and biased toward speed and demonstrated ownership in a small, fast-moving team
Nice to have: mobile development, AI-powered applications, scientific software, or developer tools experience.
Logistics and Compensation: NYC in-person (remote negotiable for the right person). See compensation in post (also negotiable for the rigth person). US work authorization required.
If you're excited about using AI to transform how science is done, we'd love to hear from you.
About 83 Sciences
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.
