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 / 713
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?
Social security officer
27%
#210 / 713
View rankingSocial security officer
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 |
|---|---|---|---|
File, contributions and the right.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Incapacity, the opinion and the one who cannot.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Benefit, the deadline and not leaving them unpaid.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Fraud, the visit and not hunting the poor.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The doctor, the discharge and not fighting for sport.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That the right one is paid, and not the one who should not.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
- pensions
- contributions
- incapacity
- benefits
- opinions
- fraud
- social-security deadlines
- medical discharges
Sources for this job· O*NET 13-1141.00· ESCO/ISCO 3353