Automation index
How much of a Mechanical engineer’s job can AI already do?
A calculation and a model come earlier. Signing a shaft that will not break does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Mechanical engineer
25%
AI can already do 25% of this job.
#239 / 623
View rankingMechanical 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 |
|---|---|---|---|
Calculate, model and simulate parts and assemblies.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Drawings, tolerances and bills of materials.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Meetings with shop, client and purchasing.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Tests, prototypes and commissioning.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Rules, quality and CE documentation.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 16% | +2.6 |
Sign, register and own the design.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Meetings with shop, client and purchasing
- Tests, prototypes and commissioning
- Rules, quality and CE documentation
- Sign, register and own the design
- ingeniero mecanico
- mechanical engineer
Sources for this job· O*NET 17-2141.00· ESCO/ISCO 2144
IS WORK OVER?
ML engineer
29%
#64 / 623
View rankingML 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 |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511