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?
Actor
17%
#575 / 623
View rankingActor
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 |
|---|---|---|---|
Break down the text, mark and memorise.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Self-tapes and casting materials.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Perform on set, stage or in voice-over.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 6% | +1.3 |
Rehearse with director and cast.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 10% | +1.6 |
Care for body, voice and emotional continuity.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Promotion, agents and life between jobs.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
| Total | 100% | 17% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Break down the text, mark and memorise
- Self-tapes and casting materials
- Perform on set, stage or in voice-over
- Rehearse with director and cast
- Promotion, agents and life between jobs
- actor
- actriz
- actor de cine
- actor de teatro
Sources for this job· O*NET 27-2011.00· ESCO/ISCO 2655