Automation index
How much of a Recycling-plant operator’s job can AI already do?
An optical sorter comes earlier. Actually sorting without mixing the bale do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Recycling-plant operator
19%
AI can already do 19% of this job.
#522 / 633
View rankingRecycling-plant operator
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 |
|---|---|---|---|
Line, optical and the bale.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Cabin, the one that sneaks in and the glass.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Jam, the belt and stopping.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Quality, the reject and not hiding it.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The peak, the lorry and not mixing.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Sort for real, and a clean bale.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
- recycling line
- bales
- sort cabin
- belts
- rejects
- glass
- optical sorters
- jams
Sources for this job· O*NET 51-9199.00· ESCO/ISCO 3132
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