Automation index
How much of a Quality engineer’s job can AI already do?
An FMEA and an 8D come earlier. Stopping the supplier and deciding if the batch travels do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Quality engineer
29%
AI can already do 29% of this job.
#63 / 623
View rankingQuality 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 |
|---|---|---|---|
FMEA, control plan and the limits.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
8D, claims and the supplier.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Audit, IATF and the awkward finding.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The line, the defect and stop or not.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
APQP, PPAP and the customer launch.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Release with judgement, and not hiding the PPM.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- FMEA
- 8D
- control plan
- IATF
- PPAP
- APQP
- supplier claims
- release batches
Sources for this job· O*NET 17-2112.00· ESCO/ISCO 2141
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