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# Materials Science Domain Expert (Train AI Models Part Time

- **Company:** Cincinnatus Llc
- **Location:** San Francisco Bay Area, CA, United States
- **Experience:** Expert
- **Contract:** Temporary to permanent
- **Skills:** Computer-Aided Design, Artificial Intelligence, Large Language Models
- **Published:** October 2, 2026
- **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=2993913075-2

## About the Role

Education: PhD in materials science, materials engineering, or a closely related discipline such as chemistry, chemical engineering, applied physics, or metallurgy. A master's degree with exceptional industrial depth may be considered.

 - Experience: 4+ years of substantive research or industrial R&D experience in materials, at a research university, a national laboratory, or an industrial research organization. Graduate coursework alone does not count.

 - Domain depth: Genuine specialization in at least one area, for example energy storage and battery materials, semiconductors and electronic materials, polymers and soft matter, structural alloys and metallurgy, characterization and microscopy, or computational materials and simulation.

 - Seniority: Clear progression to a senior level, for example Senior Scientist, Staff Scientist, Research Lead, Principal Investigator, or a senior industrial R&D role, with real ownership of research direction.

 - Research record: Peer-reviewed publications, granted patents, or shipped materials programs. Strongly preferred.

 - AI fluency: Hands-on working use of large language models in your professional work, and the judgment to tell a well-reasoned answer from a plausible-sounding wrong one.

 - Availability: Able to commit reliably to 40 hours per week for an initial engagement of 6 months.

 - Location: Living in the Bay Area, California, and able to work on-site with the client's team multiple days each week, when required. Candidates not currently based in the Bay Area must be willing to relocate there at their own cost. Relocation assistance is not provided.

 - Excellent written communication, and the ability to give precise, well-structured written feedback.

## Description

Data QA and reviews: Vet the quality of materials science knowledge work tasks and model outputs, spotting missing behaviors, thin reasoning, unsupported structure to property claims, and answers that read well but would not survive technical scrutiny.

 - Instruction specs and golden datasets: Write high-quality instruction specs, produce golden solutions to materials problems, and define new materials tasks that reflect how the work is actually done in practice.

 - Benchmarks and domain depth: Design challenging materials science tasks and evaluation sets, and help build materials-specific skills and tools together with the research team.

 - Calibration: Work with client researchers and specialists in adjacent fields to keep standards consistent, translating tacit materials judgment into explicit, teachable criteria.