Automation index
How much of a ML engineer’s job can AI already do?
Training a baseline and a notebook is fast. Shipping it, watching drift, and owning the miss is not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
ML engineer
29%
AI can already do 29% of this job.
#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
IS WORK OVER?
Olive-oil miller
22%
#348 / 623
View rankingOlive-oil miller
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 |
|---|---|---|---|
Intake, yield and the mill log.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Crush, malaxate and the decanter.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 12% | +2.2 |
Temperature, time and the fruitiness.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Cellar, the batch and the one that goes rancid.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The campaign, the tractor queue and not heating.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Cold for real, and not painting the yield.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
- crush olives
- malaxation
- decanter
- fruitiness
- the campaign
- mill yield
- oil cellar
- extraction temperature
Sources for this job· O*NET 51-3092.00· ESCO/ISCO 7514