Materials Science, Semiconductor AI Expert

Gramian Consulting

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

Posted 9/24/2026contractRemote • United States, United KingdomMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates deep technical expertise in atomic-scale materials engineering and semiconductor technologies, with a strong ability to develop structured scientific workflows and validation methods. Proficient in communicating complex technical findings effectively in client workshops.

Highest-signal resume keywords

Atomic Layer Deposition (ALD)Chemical Vapor Deposition (CVD)Physical Vapor Deposition (PVD)Computational Physics ModelingScientific Validation Methods

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Atomic-Scale Materials EngineeringSemiconductor TechnologiesScientific Ground-Truth CriteriaValidation MethodsEvaluation FrameworksDFT ModelingMD ModelingKMC ModelingAI Model Performance ImprovementScientific Scoring Rubrics

Soft Skills

Strong Verbal CommunicationStrong Written Communication

Tools & Technologies

Plasma EtchingChemical Mechanical Planarization (CMP)3D Semiconductor PackagingAdvanced Memory ArchitecturesLogic Architectures

Industry Keywords

Materials Discovery WorkflowsSimulation StabilityStoichiometryThermodynamicsAI Reasoning Traces

About the role

Key responsibilities & impact

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

What you’ll need

Deep technical expertise in atomic-scale materials engineering or semiconductor technologies.
Hands-on experience with Atomic Layer Deposition (ALD), Chemical Vapor Deposition (CVD), Physical Vapor Deposition (PVD), plasma etching, Chemical Mechanical Planarization (CMP), 3D semiconductor packaging, or 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.