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?
Audiologist
23%
#288 / 623
View rankingAudiologist
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 |
|---|---|---|---|
Audiograms, thresholds and a first fit.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 40% | +7.2 |
Trial, mould and adapt the aid.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Explain, habituation and follow-up.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Reports for ENT and notes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Repair, clean and prototype stock.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Children, older people and not overselling.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 23% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Audiograms, thresholds and a first fit
- Trial, mould and adapt the aid
- Explain, habituation and follow-up
- Reports for ENT and notes
- Repair, clean and prototype stock
- Children, older people and not overselling
- audiologo
- audiologist
Sources for this job· O*NET 29-1181.00· ESCO/ISCO 2266