Automation index
How much of a Materials engineer’s job can AI already do?
A tensile curve comes earlier. Picking the alloy so it does not fail in service do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Materials engineer
27%
AI can already do 27% of this job.
#162 / 623
View rankingMaterials engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Tests, metallography and the spec.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Shop, heat treatment and the part that fails spec.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Material choice, corrosion and fatigue.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Supplier, certificates and the doubtful batch.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The field failure and the root cause.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That it does not fail, and not swapping the material blind.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- materials tests
- metallography
- choose alloys
- corrosion
- fatigue
- heat treatment
- failure analysis
- batch certificates
Sources for this job· O*NET 17-2131.00· ESCO/ISCO 2149
IS WORK OVER?
Prompt engineer
34%
#21 / 623
View rankingPrompt engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Design prompts, tools and chains.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 58% | +13.9 |
Evals, datasets and regressions.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Integrate with product and safety limits.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to the business about a good output.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Watch hallucinations, bias and cost.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 20% | +2.8 |
Decide what the model gets, and what it does not.EstimateNo public source provides a precise figure. This is our estimate. | 12% | 16% | +1.9 |
| Total | 100% | 34% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Design prompts, tools and chains
- Evals, datasets and regressions
- Integrate with product and safety limits
- Talk to the business about a good output
- Watch hallucinations, bias and cost
- prompt engineer
- ingeniero de prompts
- llm engineer
Sources for this job· O*NET 15-1252.00· ESCO/ISCO 2512