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The AI Resilience Report helps you understand how AI is likely to impact your current or future career. Drawing on data from over 1,500 occupations, it provides a clear snapshot to support informed career decisions.
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Resilient
Last Update: 4/23/2026
Your role’s AI Resilience Score is
66.4%
Median Score
Meaningful human contribution
Measures the parts of the occupation that still require a human touch. This score averages data from up to four AI exposure datasets, focusing on the role’s resilience against automation.
Med
Long-term employer demand
Predicts the health of the job market for this role through 2034. Using Bureau of Labor Statistics data, it balances projected annual job openings (60%) with overall employment growth (40%).
Med
Sustained economic opportunity
Measures future earning potential and career flexibility. This score is a blend of total projected labor income (67%) and the role’s inherent ability to adapt to economic and technological shifts (33%).
High
This reflects the reliability of your score based on the number of data sources available for this career and how closely those sources agree on the outlook. A higher confidence means more consistent evidence from labor experts and AI models.
Limited data sources are available, or existing sources show notable disagreement on the outlook for this occupation.
Contributing sources
AI Resilience Report forMaterials Engineers
Materials Engineers are more resilient to AI impacts than most occupations, according to our analysis of 5 sources.
Materials engineering is labeled as "Resilient" because AI acts more as a helpful tool rather than a replacement. AI can speed up testing and suggest new materials, but the job still relies on human creativity, problem-solving, and decision-making skills.
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View analysisChat with CoachLatest newsMore career infoAnalysisChatNewsMoreThis role is resilient
Materials engineering is labeled as "Resilient" because AI acts more as a helpful tool rather than a replacement. AI can speed up testing and suggest new materials, but the job still relies on human creativity, problem-solving, and decision-making skills.
Read full analysisAnalysis of Current AI Resilience
Materials Engineers
Updated Quarterly • Last Update: 2/17/2026
How is AI changing Materials Engineers jobs?
Materials engineers help create and test new materials (like alloys, plastics, or ceramics) to meet product needs [1]. Right now AI tools are mostly helping rather than replacing people. For example, “generative AI” systems can suggest new material designs automatically, potentially saving time.
A recent review notes that these AI models enable “autonomous creativity” in material design and can drastically shorten development cycles [2]. Some labs even use AI to design experiments: large language models (LLMs) can read research reports and help write control software for instruments [3]. Predictive systems with sensors (sometimes called digital twins) are also coming in for monitoring performance: research on “structural health monitoring” describes AI that watches for material wear or damage in bridges and engines [4] [3].
In short, AI is beginning to augment tasks like testing, modeling, and data analysis. Tasks that heavily involve people – such as teaching students, guiding teams, or solving tricky engineering problems – still need human judgment. Current AI (like chatbots or automated graders) can assist by organizing information or simulating processes, but real teachers and engineers make the final decisions and interpretations [3] [2].
Sources
How fast is AI adoption growing for Materials Engineers?
Adopting AI in materials engineering depends on many factors. On the plus side, there are more tools and data than before. Open databases (like the Materials Project) and software libraries (MatMiner, Pymatgen) offer ready-made data and models for engineers to try [2].
These resources mean companies can get started with AI more easily. Also, materials engineering is a growing field (the government reports a “bright outlook” for these jobs through 2025 [1]), so firms might invest in AI to boost their specialized experts rather than replace them. On the other hand, building AI systems can be costly and tricky.
Many AI successes so far are in high-tech labs with robots and lots of data [3]. Smaller shops may lack the sensors or automation needed to feed AI models. Experts warn that “data unavailability” is a big hurdle in this field [2].
Finally, because materials concerns (like airplane parts) involve safety, companies will move carefully. Engineers will still double-check AI’s work.
Overall, AI is seen more as a helper than a threat here. It can do routine analysis, speed up tests, and suggest ideas [2] [3]. But human skills – like creativity, mentoring students, and making final judgments – remain very important.
Many news and academic reports agree: the future is AI-augmented materials engineering, not AI-replaced rewarding careers [2] [3].
Sources
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More Career Info
Career: Materials Engineers
They create and test materials to make products stronger, lighter, or better, like designing new metals for cars or plastics for smartphones.
Parent Careers
Employment & Wage Data
Median Wage
$108,310
Jobs (2024)
23,000
Growth (2024-34)
+5.7%
Annual Openings
1,500
Education
Bachelor's degree
Experience
None
Source: Bureau of Labor Statistics, Employment Projections 2024-2034
Task-Level AI Resilience Scores
AI-generated estimates of task resilience over the next 3 years
1
Design processing plants and equipment.
2
Supervise the work of technologists, technicians, and other engineers and scientists.
3
Write for technical magazines, journals, and trade association publications.
4
Guide technical staff engaged in developing materials for specific uses in projected products or devices.
5
Plan and implement laboratory operations for the purpose of developing material and fabrication procedures that meet cost, product specification, and performance standards.
6
Replicate the characteristics of materials and their components with computers.
7
Review new product plans and make recommendations for material selection based on design objectives, such as strength, weight, heat resistance, electrical conductivity, and cost.
Tasks are ranked by their AI resilience, with the most resilient tasks shown first. Core tasks are essential functions of this occupation, while supplemental tasks provide additional context.
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