[Hiring] Materials Science & Semiconductor AI Expert @Gramian Consulting Group

Role Description

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.

LOCATION: Remote — United States (excluding specified states) and United Kingdom.

• 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.

Qualifications

• 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.