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 / 683
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?
University careers adviser
27%
#181 / 683
View rankingUniversity careers adviser
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 |
|---|---|---|---|
Workshop, the CV and the letter.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Appointment, the one who does not know and the one who wants everything.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Placement, the firm and not selling smoke.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Fair, the contact and the one who will not approach.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The dean, insertion and not dressing the rate.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
A plan, and not panic.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- university CVs
- cover letters
- placements
- jobs fairs
- graduate insertion
- careers appointments
- not sell smoke
- workshops
Sources for this job· O*NET 21-1012.00· ESCO/ISCO 2423