Materials Science Domain Expert

Weekday AI; California City (CA); Hybrid; USD 96,000 - 152,000; Full time

Job summary

Weekday AI is seeking a highly specialized Materials Science Domain Expert to contribute to an advanced GenAI initiative. You will evaluate knowledge tasks and AI outputs, develop reference solutions, and create rigorous benchmarks for high-quality reasoning.

This full-time role requires 40 hours weekly for an initial six-month period, with a hybrid location in the Bay Area, California. Relocation is not covered. On-site presence is required several days per week.

Qualifications

• PhD or an exceptional Master's with relevant depth in materials science.
• At least 4 years of substantive R&D experience in materials science.
• Demonstrated domain expertise in at least one materials area.
• Experience with AI tools and distinguishing rigorous reasoning.
• Ability to work in the Bay Area on-site several days per week.

Responsibilities

• Review and assess AI-generated materials science outputs for accuracy and relevance.
• Develop instruction specifications and golden solutions.
• Design challenging tasks and benchmarks for AI evaluation.
• Collaborate with researchers and translate expertise into structured criteria.
• Provide precise written feedback to improve AI solutions.

Skills

Energy materials

Semiconductors

Polymers

Metallurgy

Characterization

Computational materials

Nanomaterials

AI fluency

Senior leadership

Research leadership

Education

PhD in Materials Science

Master's with depth in materials science

Job description

This role is for one of our clients

Compensation: $70 - $110 per hour

We are seeking an experienced Materials Science Domain Expert to contribute to an advanced GenAI initiative focused on improving how AI systems understand, reason about, and solve complex materials science and materials engineering problems.

Your technical expertise will be central to this role. You will evaluate materials science knowledge tasks and AI-generated outputs, develop detailed instructions and reference solutions, and create rigorous benchmarks that define what high-quality technical reasoning looks like.

We are looking for a hands-on materials specialist with deep expertise in a specific area of materials science or engineering, rather than a broad generalist.

This is a full-time engagement requiring 40 hours per week for an initial six-month period. You will collaborate closely with research and program teams and work within established technical workflows and enterprise tools.

Location: Hybrid role based in the Bay Area, California. Candidates must currently live in the Bay Area and be available to work on-site multiple days per week when required. This is not a fully remote position. Candidates outside the Bay Area must be willing to relocate at their own expense before the engagement begins. Relocation assistance is not provided.

Key Responsibilities

Technical Data Quality & Evaluation

• Review and assess materials science knowledge tasks and AI-generated technical outputs for accuracy, depth, scientific validity, and practical relevance.
• Identify incomplete reasoning, unsupported structure-property relationships, incorrect technical assumptions, and conclusions that may appear convincing but fail expert-level scrutiny.
• Evaluate whether AI-generated solutions align with established scientific principles, engineering practices, and real-world materials workflows.

Instruction & Reference Solution Development

• Write clear and comprehensive instruction specifications that define expected approaches and outcomes for materials science problems.
• Develop high-quality reference or "golden" solutions for complex materials science and engineering scenarios.
• Create new technical tasks that accurately reflect how materials scientists and engineers approach real-world research, development, characterization, and optimization challenges.

Benchmark & Evaluation Development

• Design challenging materials science tasks and evaluation datasets that test scientific reasoning and technical expertise.
• Contribute to the development of materials-specific benchmarks, capabilities, and evaluation tools.
• Establish meaningful criteria for assessing AI performance across different materials science and engineering applications.

Expert Calibration & Collaboration

• Collaborate with researchers and subject matter experts from related scientific and engineering disciplines.
• Help maintain consistency and accuracy across evaluation standards and technical datasets.
• Translate practical materials science expertise and professional judgment into explicit, structured, and teachable evaluation criteria.
• Provide precise written feedback to improve the technical quality and reliability of AI-generated solutions.

Core Qualifications

• Education: PhD in Materials Science, Materials Engineering, or a closely related discipline such as Chemistry, Chemical Engineering, Applied Physics, Metallurgy, or a related technical field.
• A Master's degree with exceptional industrial or research depth may be considered for highly experienced candidates.
• Experience: At least 4 years of substantive research or industrial R&D experience in materials science, materials engineering, or a closely related field.
• Relevant experience may come from a research university, national laboratory, industrial research organization, or materials-focused technology company.
• Graduate coursework or academic training alone does not satisfy the professional experience requirement.
• Domain Expertise: Demonstrated specialization in at least one materials-focused area, such as:
• Energy storage and battery materials
• Semiconductors and electronic materials
• Polymers and soft matter
• Structural alloys and metallurgy
• Materials characterization and microscopy
• Computational materials science and simulation
• Nanomaterials and advanced materials
• Functional or engineered materials
• Seniority: Demonstrated progression into a senior technical or research position, such as Senior Scientist, Staff Scientist, Research Lead, Principal Investigator, or senior industrial R&D leadership.
• Proven ownership of research direction, technical programs, materials development initiatives, or significant research projects.
• Research Record: Peer-reviewed publications, granted patents, technology development, or successful materials programs are strongly preferred.
• AI Fluency: Hands-on professional experience using large language models or AI tools, along with the ability to distinguish technically rigorous reasoning from plausible but scientifically incorrect outputs.
• Availability: Ability to commit reliably to 40 hours per week for an initial six-month engagement.
• Location: Must reside in the Bay Area, California, and be able to work on-site multiple days per week when required. Candidates outside the area must be willing to relocate at their own expense. Relocation assistance is not provided.
• Excellent written communication skills and the ability to provide precise, structured, and actionable technical feedback.

Preferred Qualifications

• Experience working on multidisciplinary materials research involving chemistry, physics, engineering, or computational methods.
• Familiarity with modern materials characterization techniques, simulation methodologies, or experimental workflows.
• Experience translating research findings into practical engineering or commercial applications.
• Background in advanced materials development, materials optimization, or technology commercialization.
• Experience reviewing technical documentation, scientific research, engineering analyses, or AI-generated technical content.
• Strong interest in the application of artificial intelligence to scientific research and engineering.

What You’ll Contribute

You will help transform expert materials science knowledge into structured tasks, reference solutions, evaluation frameworks, and benchmarks for next-generation AI systems.

Your expertise will help ensure that AI-generated materials science solutions are not merely fluent or convincing, but scientifically sound, technically rigorous, logically reasoned, and relevant to real-world research and engineering practice.

Equal Opportunity

We are committed to providing equal employment opportunities to all qualified candidates. Employment decisions are made without regard to legally protected characteristics. Reasonable accommodations are available for qualified individuals throughout the application and hiring process.