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 / 643
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?
Dosimetrist
22%
#336 / 643
View rankingDosimetrist
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 |
|---|---|---|---|
CT, contours and the plan.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Organ at risk, the curve and not overshooting.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Physics, the check and the one that does not fit.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Fraction, the setup and the patient who is not the same.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The oncologist, the trade-off and not signing blind.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
The tumour gets it, and the healthy organ does not.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
- dose plans
- contours
- organs at risk
- dosimetry check
- fractions
- setup
- sim CT
- DVH curves
Sources for this job· O*NET 29-2036.00· ESCO/ISCO 3211