Materials Science or Semiconductor Physics Expert

About the Role

We are seeking an experienced Materials Science or Semiconductor Physics Expert to evaluate and benchmark advanced AI models against real-world materials discovery workflows. You will collaborate with client R&D teams to transform atomic-scale engineering challenges into structured, verifiable test scenarios, define scientific grading criteria, and diagnose AI model performance.

The ideal candidate combines deep expertise in semiconductor materials, thin-film processes, and computational physics with the ability to translate complex scientific problems into reproducible evaluation workflows.

Key Responsibilities

• Participate in technical discovery sessions with client R&D teams.

• Map and deconstruct end-to-end materials discovery workflows into discrete subprocesses.

• Convert real-world materials engineering challenges into structured test scenarios.

• Define inputs, constraints, expected outputs, and verified golden reference solutions.

• Develop scientific scoring rubrics and programmatic validation rules.

• Validate criteria such as stoichiometry, thermodynamics, and simulation stability.

• Inspect step-by-step AI reasoning traces to identify failure patterns and root causes.

• Distinguish scientific errors from incorrect assumptions, implementation issues, or evaluation defects.

• Define domain-specific data generation requirements and synthetic physics pipelines.

• Contribute to discussions on fine-tuning strategies and methods for improving AI model performance.

• Communicate technical findings and recommendations during client workshops.

Requirements

• Deep technical expertise in atomic-scale materials engineering or semiconductor technologies.

• Hands-on experience with one or more of the following:

  • Atomic Layer Deposition (ALD)

  • Chemical Vapor Deposition (CVD)

  • Physical Vapor Deposition (PVD)

  • Plasma etching

  • Chemical Mechanical Planarization (CMP)

  • 3D semiconductor packaging

  • Advanced memory or logic architectures

• Familiarity with computational physics or chemistry modeling workflows, including DFT, MD, or kMC.

• Ability to formulate complex, open-ended scientific workflows into structured and verifiable problem statements.

• Experience defining scientific ground-truth criteria, validation methods, or evaluation frameworks.

• Strong verbal and written English communication skills for technical and business workshops.