Research and study the structures and chemical properties of various natural and synthetic or composite materials, including metals, alloys, rubber, ceramics, semiconductors, polymers, and glass. Determine ways to strengthen or combine materials or develop new materials with new or specific properties for use in a variety of products and applications. Includes glass scientists, ceramic scientists, metallurgical scientists, and polymer scientists.
U.S. Workers
8,330
Median Salary
$104,160
10-Year Growth
+4.9%
Annual Openings
600
Typical entry: Bachelor's degree
15 of 15 tasks have some AI capability
Exposure Trend
This score reflects estimated AI technical capability for tasks in this occupation. It does not predict employment changes, and it does not account for company-specific constraints, regulation, or adoption barriers.
Prepare reports, manuscripts, proposals, and technical manuals for use by other scientists and requestors, such as sponsors and customers.
AI: Fully automatable - AI can compose reports, manuscripts, proposals, and technical manuals from data and guidance to a publishable standard, enabling full automation of the writing and documentation aspects of this task.
Recommend materials for reliable performance in various environments.
AI: Fully automatable - AI-driven materials selection tools, databases, and simulation/optimization pipelines can fully recommend suitable materials for reliable performance in many environments.
Conduct research on the structures and properties of materials, such as metals, alloys, polymers, and ceramics, to obtain information that could be used to develop new products or enhance existing ones.
AI: Partial - AI excels at simulation, modeling, and data analysis for materials research and can accelerate discovery, but experimental synthesis and empirical validation required for research outcomes still need human-led laboratory work.
Perform experiments and computer modeling to study the nature, structure, and physical and chemical properties of metals and their alloys, and their responses to applied forces.
AI: Partial - AI can perform extensive computer modeling and drive automated experiments, but cannot autonomously carry out all physical experimental tasks and interpret novel, ambiguous lab outcomes across contexts.
Plan laboratory experiments to confirm feasibility of processes and techniques used in the production of materials with special characteristics.
AI: Partial - AI can design and optimize laboratory experiment plans and suggest protocols, yet human oversight is still needed for safety, practical implementation, and unanticipated constraints.
Determine ways to strengthen or combine materials or develop new materials with new or specific properties for use in a variety of products and applications.
AI: Partial - AI and ML greatly accelerate discovery and propose candidate material combinations and strengthening strategies, but experimental validation and scale-up remain human-led and context dependent.
Teach in colleges and universities.
AI: Partial - AI can deliver lectures, generate curricula, and grade at scale, but the full scope of university teaching—mentorship, accreditation, research supervision, and in-person labs—still requires human faculty.
Devise testing methods to evaluate the effects of various conditions on particular materials.
AI: Partial - AI can devise and simulate testing methods and analyze results, but development of validated, regulatory-accepted test procedures and hands-on method development needs human expertise.
Research methods of processing, forming, and firing materials to develop such products as ceramic dental fillings, unbreakable dinner plates, and telescope lenses.
AI: Partial - AI can model processing, predict outcomes, and guide automated trials, but practical processing development, specialized tooling, and manufacturing trials require human-led experimentation.
Confer with customers to determine how to tailor materials to their needs.
AI: Partial - AI can gather requirements, propose tailored material solutions, and support customer interactions, but nuanced negotiation, trust-building, and complex integration decisions typically need human involvement.
Test individual parts and products to ensure that manufacturer and governmental quality and safety standards are met.
AI: Partial - AI can automate many tests, analyze quality data, and flag nonconformances, but complex certification tests, physical inspections, and on-site validation often require humans and specialized equipment.
Supervise and monitor production processes to ensure efficient use of equipment, timely changes to specifications, and project completion within time frame and budget.
AI: Partial - AI can monitor, optimize, and coordinate production processes and scheduling, but comprehensive supervision including personnel management, strategic trade-offs, and stakeholder decisions still needs human leadership.
Test metals to determine conformance to specifications of mechanical strength, strength-weight ratio, ductility, magnetic and electrical properties, and resistance to abrasion, corrosion, heat, and cold.
AI: Partial - Automated test rigs and AI can run and analyze many metallurgical tests, but physical sample handling, setup variability, and expert judgment about atypical results still require human oversight in 2025.
Test material samples for tolerance under tension, compression, and shear to determine the cause of metal failures.
AI: Partial - Machines can perform tension/compression/shear tests and AI can analyze data to identify failure modes, but complex forensic interpretation and nonstandard sample preparation typically need human expertise.
Visit suppliers of materials or users of products to gather specific information.
AI: Partial - Remote interviews, automated data requests, and virtual site assessments can gather much information, but in-person relationship-building, nuanced observation, and some on-site verification remain difficult to fully automate.