Mostly Resilient

Last Update: 8/30/2026

AI Resilience Score for Materials Engineers:

59.9%

Median Score

Meaningful human contribution

Med

Long-term employer demand

Med

Sustained economic opportunity

High

Our confidence in this score:
Low-medium

Contributing sources

Methodology and Scoring Rationale

To score how resilient materials engineering is to AI, we ask one question in three parts:

First, how much of the job still needs a human, read from five AI-exposure sources: our own AI Resilience Model, Anthropic's Observed Exposure, Microsoft's AI Applicability, Will Robots Take My Job, and OpenAI Signals. We call this dimension Meaningful Human Contribution (MHC) and weight it at 40%.

Next, whether employers will keep hiring for this job over the long term. This dimension, which we call Long-term Employer Demand (LTE), is calculated from BLS data and weighted at 30%.

Last, whether pay and mobility will hold up. We use wage bill and adaptive capacity data from independent researchers (Althoff & Reichardt, 2026; Manning & Aguirre, 2026). We call this dimension Sustained Economic Opportunity (SEO) and weight it at 30%.

For materials engineers, six of eight sources had data, with Anthropic and Adaptive Capacity missing. AI exposure was split: AI Resilience Model rated it Low while Will Robots Take My Job and OpenAI Signals rated it High, keeping confidence at low-medium. Strong pay signals lifted the score, landing materials engineers at "Mostly Resilient."

AI Resilience Report forMaterials Engineers

$112,860 median salary1,300 annual openingsSOC Code: 17-2131.00

Materials Engineers are somewhat more resilient to AI impacts than most occupations, according to our analysis of 6 sources.

Materials engineering is labeled "Mostly Resilient" because AI is changing *how* engineers work rather than replacing them altogether. Tools like self-driving labs and simulation software are taking over repetitive tasks (like running thousands of experiments), but the work of supervising teams, making safety calls on products like jet parts or medical implants, and translating results into real-world solutions still requires human judgment and accountability.

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This role is mostly resilient

Materials engineering is labeled "Mostly Resilient" because AI is changing *how* engineers work rather than replacing them altogether. Tools like self-driving labs and simulation software are taking over repetitive tasks (like running thousands of experiments), but the work of supervising teams, making safety calls on products like jet parts or medical implants, and translating results into real-world solutions still requires human judgment and accountability.

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Analysis of Current AI Resilience

Materials Engineers

Updated Quarterly

Analysis
Suggested Actions
State of Automation

How is AI changing Materials Engineers jobs?

Right now, AI is mostly augmenting materials engineers — helping them work faster — rather than replacing them. The biggest change is in the "digital twin" part of the job: using computers to simulate how new metals, plastics, or ceramics will behave. ASM International's Materials Informatics Technical Committee [1] is pushing "pre-competitive collaboration among its members to expedite the industry-wide adoption of artificial intelligence and machine learning in materials science." Trade groups are all-in: the Minerals, Metals & Materials Society is hosting the 4th World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2026) [2] to "focus on the role of artificial intelligence (AI) in materials science and engineering and related manufacturing processes."

Behind the scenes, "self-driving labs" combine AI with robots that run experiments day and night. At Argonne National Laboratory, AI can "run thousands of experiments, evaluate results, identify patterns and determine next steps," [3] freeing researchers for more complex tasks. The Materials Research Society is highlighting the same trend, spotlighting autonomous material discovery specialists ranging "from those in lab automation and high-throughput material synthesis to experts in automated characterization and materials testing" [4].

The tasks least touched — supervising teams, consulting with executives, and guiding technicians — still need human judgment.

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AI Adoption

How fast is AI adoption growing for Materials Engineers?

Adoption is speeding up because the payoff is huge. McKinsey's 2025 R&D report [5] argues leaders shouldn't just "automate steps in that legacy process; you need to fundamentally rethink the way that products are concepted, designed, and taken to market end to end." Faster simulations mean cheaper prototypes and quicker breakthroughs in batteries, chips, and aerospace alloys.

But the field won't flip overnight. Materials work involves safety-critical products (jet parts, medical implants) that require certification, testing standards, and human accountability. Labor demand is also solid: the Bureau of Labor Statistics projects materials engineer employment growing about 5.7% from 2024 to 2034, adding roughly 1,300 jobs [6], with median wages above $108,000.

The takeaway for students: learn the science and learn to work with AI tools. The engineers who thrive will be the ones who can guide the machines, verify their answers, and translate results into real-world products.

Sources

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Will AI replace Materials Engineers?

Will AI replace Materials Engineers?

No. We don't think AI will replace Materials Engineers, though we do expect the job to change.

We gave this career a 59.9% AI Resilience Score, meaning it holds up better than most. The biggest shift is already happening in simulation and discovery: self-driving labs at places like Argonne National Laboratory can run thousands of experiments automatically, evaluate results, and identify patterns [3]. That is genuinely transformative. But it frees engineers for harder problems rather than eliminating them. The tasks AI handles least well, such as supervising teams, consulting with executives, and making judgment calls on safety-critical products like jet parts or medical implants, still need a human in the loop.

The economic picture supports optimism too. The Bureau of Labor Statistics projects materials engineer employment growing about 5.7% from 2024 to 2034, adding roughly 1,300 jobs, with median wages above $108,000 [6]. Industry groups are leaning into the human-plus-AI model: the Materials Research Society is spotlighting autonomous material discovery as a growing specialty [4], and McKinsey argues the real opportunity is rethinking how products are conceived and designed end to end, not just automating old steps [5].

The engineers who thrive will be the ones who can guide AI tools, verify their outputs, and translate results into real products. That combination is hard to automate.

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Latest AI news for Materials Engineers

The recommended articles highlight the growing intersection of AI and materials engineering, showcasing new job opportunities and advancements in the field. For instance, Applied Materials' expansion in Singapore is creating 1,000 jobs focused on AI-driven semiconductor technology, emphasizing the demand for skilled materials engineers. Additionally, NUS's collaboration to apply AI in semiconductors reflects the need for engineers who can integrate AI into materials science. These developments suggest that embracing AI will enhance career prospects and resilience in the evolving materials engineering landscape.

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

$112,860

Jobs (2025)

23,800

Growth (2025-35)

+7.5%

Annual Openings

1,300

Education

Bachelor's degree

Experience

None

Source: Bureau of Labor Statistics, Employment Projections 2025-2035

Task-Level AI Resilience Scores

AI-generated estimates of task resilience over the next 3 years

1

95% ResilienceSupplemental

Teach in colleges and universities.

2

92% ResilienceCore Task

Supervise the work of technologists, technicians, and other engineers and scientists.

3

92% ResilienceSupplemental

Present technical information at conferences.

4

90% ResilienceCore Task

Guide technical staff in developing materials for specific uses in projected products or devices.

5

88% ResilienceCore Task

Plan and evaluate new projects, consulting with other engineers and corporate executives, as necessary.

6

85% ResilienceCore Task

Supervise production and testing processes in industrial settings, such as metal refining facilities, smelting or foundry operations, or nonmetallic materials production operations.

7

82% ResilienceCore Task

Plan and implement laboratory operations to develop material and fabrication procedures that meet cost, product specification, and performance standards.

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.

The AI Resilience Report is a project from CareerVillage.org®, a registered 501(c)(3) nonprofit.

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