Materials Science & Semiconductor AI Expert

Gramian Consulting Group

2d•Remote

About The Position

Gramian Consultancy is seeking an experienced Materials Science or Semiconductor Physics Expert to evaluate and benchmark advanced AI models against real-world materials discovery workflows. The role involves collaborating with client R&D teams to transform atomic-scale engineering challenges into structured, verifiable test scenarios, defining scientific grading criteria, and diagnosing AI model performance. The ideal candidate will possess deep expertise in semiconductor materials, thin-film processes, and computational physics, coupled with the ability to translate complex scientific problems into reproducible evaluation workflows.

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.

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.