Founding AI/ML Engineer

83 Sciences; New York (NY); On-site; USD 130,000 - 180,000; Full time

Job summary

83 Sciences in New York City is seeking a founding AI engineer to 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.

You will ship enterprise-ready products across our platform, develop multimodal AI systems, and shape data pipelines and technical direction as an early team member. NYC in-person collaboration expected.

Qualifications

• 1+ years of experience managing technical projects at startups or large research labs.
• Deep in at least two: geometric deep learning, generative models, or ML interatomic potentials.
• Track record shipping enterprise-ready products to real users, end-to-end.
• Comfort across the stack with ownership in a small, fast-moving team.

Responsibilities

• Ship enterprise-ready products across our platform: data capture, structured experimental records, querying, and analysis tools.
• Build our AI systems: multimodal pipelines, agents that reason over a lab's full history, and predictive models.
• Work directly with research partners and scientists to iterate based on real usage.
• Help shape model architecture, data pipelines, and technical direction as an early team member.
• Build the data engine that turns messy experimental data into training-grade datasets.

Skills

Project management

End-to-end product shipping

Ownership mindset

Geometric deep learning

Generative models

ML interatomic potentials

Job description

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.

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.