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🌍Materials Science Expert, Remote- Contract

  • Remote, United States

Remote

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Job details

Workplace
Remote
Posting language
English

Original job description

Role Title: Materials Science Expert Role Type: Contract Location: Remote

Required Skills:

  • Technical Depth in a Subdomain
  • Engineering Judgment Under Constraints
  • Knowing When an Answer Is Wrong
  • Material-science

Opportunity Snapshot:

Commitment: Flexible - fits alongside your current role Location: Fully remote You’ll need: Master’s or PhD in materials science or a closely related field, plus principal-level industry experience or a faculty appointment Your tasks: Build challenging technical problems from your own domain and define what makes an answer correct

You'll be working on:

The problems where the textbook answer isn’t enough, where the right approach depends on reading the constraints correctly, where a plausible-looking solution is quietly wrong. You’ll build those problems, provide the correct analysis, and define what separates a right answer from one that only looks

What you’ll do:

  • Design difficult technical problems from your own area - structure-property relationships, processing, characterization, failure analysis, computational materials - with realistic constraints and supporting data.
  • Provide the correct solution and walk through the reasoning that gets there.
  • Define objective criteria for correctness, so another expert reviewing the work would reach the same verdict.
  • Review problems built by other contributors for technical accuracy and soundness of method.
  • Flag problems that turn out to be ambiguous or ungradable rather than submitting them.

What we’re looking for:

  • Master’s or PhD in materials science, metallurgy, ceramics, polymer science, condensed matter physics, or a closely related field.
  • Principal, staff, or fellow-level experience in industrial or national lab research, or a faculty appointment at a leading university.
  • Deep technical depth in at least one materials subdomain, with hands-on ownership of the work.
  • Able to explain why an answer is correct clearly enough that another scientist could verify it.
  • Publications, patents, or a record of technical leadership is a strong plus.
  • AI or LLM evaluation experience is a plus, but not required.

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