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 / 663
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?
Biomedical equipment technician
19%
#565 / 663
View rankingBiomedical equipment technician
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 |
|---|---|---|---|
Ticket, the inventory and the calibration.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
ICU, the ventilator and not switching off the one that is ventilating.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Theatre, the stack and the one that fails at induction.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Part, the spare and the one that does not arrive.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The clinician, the rush and not leaving the kit half done.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
That it works at three, and signing the calibration.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 19% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- calibrate biomedical kit
- ventilators
- theatre stacks
- clinical inventory
- spares
- ICU
- not switch off the one ventilating
- tickets
Sources for this job· O*NET 49-9062.00· ESCO/ISCO 3211