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?
Tiler
17%
#588 / 623
View rankingTiler
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 |
|---|---|---|---|
Layout, levels and ordering tile.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 30% | +4.8 |
Cut, set and grout.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 8% | +1.9 |
Substrates, falls and awkward points.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Site, client and not staining the house.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Quantities, extras and logs.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 22% | +3.1 |
Not leaving a floor that sounds hollow.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 17% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- build walls
- plaster
- site work
- Layout, levels and ordering tile
- Cut, set and grout
- Substrates, falls and awkward points
- Site, client and not staining the house
- Quantities, extras and logs
- Not leaving a floor that sounds hollow
- solador
Sources for this job· O*NET 47-2044.00· ESCO/ISCO 7122