Automation index
How much of a Semiconductor technician’s job can AI already do?
A wafer recipe comes earlier. The batch coming out without eating the cleanroom do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Semiconductor technician
22%
AI can already do 22% of this job.
#394 / 633
View rankingSemiconductor technician
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 |
|---|---|---|---|
Recipe, tool and the batch.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Room, the suit and the one that contaminates.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Metrology, the defect and stopping.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Shift, the yield and not hiding it.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The engineer, the change and not touching blind.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
The batch comes out, and the room intact.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- wafers
- cleanroom
- yield
- metrology
- process recipes
- contamination
- chip batches
- suits
Sources for this job· O*NET 51-9141.00· ESCO/ISCO 3114
IS WORK OVER?
ML engineer
29%
#64 / 633
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