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?
Excavator operator
18%
#564 / 623
View rankingExcavator operator
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 |
|---|---|---|---|
Set-out, levels and the machine log.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Dig, load and the batter that comes in.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 6% | +1.4 |
Services, the blind pipe and stopping in time.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Maintenance, grease and the pin that snaps.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The labourer, the lorry and covering nobody.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
Not breaking what is buried, and leaving a clean formation.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 10% | +1.4 |
| Total | 100% | 18% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- dig with excavator
- load lorries
- batters
- buried services
- set-out levels
- grease the machine
- formation
- site GPS
Sources for this job· O*NET 47-2073.00· ESCO/ISCO 8342