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Ai For Materials Jobs (NOW HIRING)

TripleTen

$40 - $80/hr

Most in demand: AI for Manual QA, AI for Automated Testing, AI for Mobile QA (Kotlin / Android ... Create educational content, assessments, and project-based learning materials * Design practical ...

C5R

Research Associate (Materials)

San Francisco, CA · On-site

$80 - $100/hr

C5R is building facilities that connect frontier AI to the physical world. We see this connection as a force for good in expanding humanity's tech tree. We are hiring a materials scientist to design ...

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Ai For Materials information

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$38K

$100.7K

$158K

How much do ai for materials jobs pay per year?

As of Sep 8, 2026, the average yearly pay for ai for materials in the United States is $100,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $80,000.00 and $116,500.00 per year, depending on experience, location, and employer.

What is AI for Materials?

AI for Materials refers to the use of artificial intelligence and machine learning techniques to accelerate the discovery, design, and optimization of new materials. By analyzing large datasets and predicting properties, AI helps scientists identify promising materials for applications such as energy, electronics, and manufacturing. This approach significantly reduces the time and cost associated with traditional experimental methods, making materials research more efficient and innovative.

What are some typical challenges faced by professionals working in AI for Materials Science, and how can these be addressed?

Professionals in AI for Materials Science often encounter challenges such as limited high-quality data, integrating domain knowledge with machine learning models, and ensuring model interpretability for scientific insights. Collaborating closely with materials scientists and data engineers helps bridge knowledge gaps and improve dataset quality. Additionally, staying updated with the latest AI techniques and actively participating in interdisciplinary teams can enhance problem-solving and foster innovation in this rapidly evolving field.

What are the key skills and qualifications needed to thrive as an AI for Materials specialist, and why are they important?

To thrive as an AI for Materials Specialist, you need a solid background in materials science, data analysis, and proficiency in machine learning, often supported by an advanced degree in materials engineering, chemistry, or computer science. Familiarity with programming languages like Python, machine learning frameworks (e.g., TensorFlow, PyTorch), and materials databases is typically required. Strong problem-solving skills, collaboration, and effective communication are vital soft skills for interpreting complex data and working with interdisciplinary teams. These competencies enable the effective application of AI to accelerate materials discovery and innovation in research or industrial settings.

What is the difference between Ai For Materials vs Materials Scientist?

AspectAi For MaterialsMaterials Scientist
Required CredentialsTypically requires knowledge of AI, machine learning, and materials science fundamentalsRequires a degree in materials science, chemistry, or related fields, often with advanced degrees
Work EnvironmentPrimarily in tech labs, R&D centers, or software development teamsLaboratories, research institutions, or industrial settings
Industry UsageUsed in materials discovery, simulation, and optimization through AI toolsFocuses on experimental research, characterization, and development of materials

While Ai For Materials involves applying AI techniques to materials research, Materials Scientists focus on experimental and theoretical study of materials. Both roles often collaborate but differ in their core skills and work environments.

Infographic showing various Ai For Materials job openings in the United States as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $100,738 per year, or $48.4 per hour.

Materials Science Domain Expert

Weekday AI

California City, CA • On-site

$70 - $110/hr

Full-time

Posted 6 days ago

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.
Requirements
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.

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