How much of this occupation’s work can be materially affected by current AI systems.
How likely exposure is to translate into reduced human demand.
Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured
Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.
What is driving the score?
Occupation scores are built from the task mix—not a single prediction about a job title.
Where AI can do more
Routine, digitized, and highly repeatable tasks face the greatest pressure.
- Design and direct the testing or control of processing procedures.77
- Modify properties of metal alloys, using thermal and mechanical treatments.77
- Evaluate technical specifications and economic factors relating to process or product design objectives.76
- Determine appropriate methods for fabricating and joining materials.76
Where people still matter
These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.
- Plan and evaluate new projects, consulting with other engineers and corporate executives, as necessary.01
- Monitor material performance, and evaluate its deterioration.02
- Supervise the work of technologists, technicians, and other engineers and scientists.03
- Design and direct the testing or control of processing procedures.04
- Evaluate technical specifications and economic factors relating to process or product design objectives.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Materials Scientists
Closely related work
Compare these careers →Manufacturing Engineers
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Compare these careers →Mechanical Engineers
Closely related work
Compare these careers →Nanosystems Engineers
Related work
Compare these careers →Nanotechnology Engineering Technologists and Technicians
Related work
Compare these careers →Adoption and labour-market outlook
Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.
O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.
