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?
Forest ranger
18%
#541 / 623
View rankingForest ranger
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 |
|---|---|---|---|
Logs, GIS and penalty files.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Patrol, poaching and fires.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 10% | +2.2 |
Neighbours, shepherds and councils.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Sampling, paths and signage.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Duties, weather and terrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
Not arriving late to a column of smoke.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 18% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Logs, GIS and penalty files
- Patrol, poaching and fires
- Neighbours, shepherds and councils
- Sampling, paths and signage
- Duties, weather and terrain
- Not arriving late to a column of smoke
- agente forestal
- agente medioambiental
- forest ranger
Sources for this job· O*NET 33-3031.00· ESCO/ISCO 5419