Automation index
How much of a Grill cook’s job can AI already do?
A doneness chart comes earlier. Marking on the grill and serving it at the point do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Grill cook
19%
AI can already do 19% of this job.
#478 / 633
View rankingGrill cook
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 |
|---|---|---|---|
Cut, ageing and the requested doneness.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Grill, mark and the fire that dies.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Rest, plate and the one that goes cold.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
List, shrink and the expensive cut.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The full room and not working too far ahead.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
At the point, and not drying the cut.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
- mark on the grill
- doneness
- ageing
- rest
- plate
- cuts
- grill shrink
- the fire
Sources for this job· O*NET 35-2014.00· ESCO/ISCO 5120
IS WORK OVER?
ML engineer
29%
#64 / 633
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