Join an innovative initiative to create realistic enterprise environments for training and evaluating frontier AI agents. This role calls for experienced materials science professionals from Fortune 500 R&D organizations, major national labs, and top R1 universities. You will leverage your expertise in materials R&D, characterization, process development, or reliability engineering to construct a high-fidelity digital workspace that reflects the tools, files, and workflows of a serious materials-science organization, while also designing tasks that challenge state-of-the-art AI.

• Build a realistic digital workspace based on your daily Drive folders, including experimental write-ups, characterization reports, simulation results, technology transfer memos, IP disclosures, project proposals, and relevant email threads, while incorporating platforms that support your work (e.g., VASP, Materials Studio (Biovia), LabVantage LIMS, ANSYS Fluent).
• Design multi-step tasks that reflect your real workflows, requiring navigation through multiple applications, files, and stakeholders, effectively challenging frontier AI agents.
• Collaborate with fellow materials-science experts to design the environment, shape task scope, and review scenarios for realism and rigor.
• Work asynchronously with research teams to refine task designs and evaluation criteria for materials-science agent benchmarks.
• Contribute to frontier AI research and benchmarking, with your work directly informing how leading labs train and evaluate the next generation of AI systems.

• MS or PhD in materials science, chemistry, physics, or a related discipline.
• 3+ years of full-time experience at a Fortune 500 R&D organization, national lab, or R1 university materials research group.
• Background in one or more areas such as metals, polymers, ceramics, composites, or semiconductor materials.
• Experience with characterization techniques (XRD, SEM/TEM, spectroscopy, thermal analysis).
• Knowledge of computational materials science, DFT, or MD.
• Experience in process development and scale-up.
• Expertise in reliability or failure analysis in advanced materials.
• Familiarity with VASP, Materials Studio (Biovia), LabVantage or LabWare LIMS, and ANSYS Fluent or STAR-CCM+.
• Strong analytical thinking and writing skills, with the ability to translate materials-science workflows into structured task specifications.

This project will start with an effective hourly rate, transitioning to a compensation model based on the throughput of quality work rather than a flat accruing hourly rate.

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