Startups · AI

Full-Stack Software Engineer

83 Sciences · New York, NY, US / Remote (US) · Remote

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About 83 Sciences

AI-native materials discovery powered by unpublished experimental data. Backed by Y Combinator.

About 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 they'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
More about this 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.

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 Most experimental data (failed runs, unpublished results, raw instrument output) never gets captured.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

  • 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....

Read the full posting on 83 Sciences's site ↗

Engineering

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