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?
Barista
28%
#84 / 623
View rankingBarista
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 |
|---|---|---|---|
Prepare or serve hot or cold beverages, such as coffee, espresso drinks, blended coffees, or teas.EstimateNo public source provides a precise figure. This is our estimate. | 9% | 28% | +2.5 |
Receive and process customer payments.EstimateNo public source provides a precise figure. This is our estimate. | 8% | 28% | +2.2 |
Take customer orders and convey them to other employees for preparation.EstimateNo public source provides a precise figure. This is our estimate. | 8% | 28% | +2.2 |
Describe menu items to customers, or suggest products that might appeal to them.EstimateNo public source provides a precise figure. This is our estimate. | 7% | 28% | +2 |
Serve prepared foods, such as muffins, biscotti, or bagels.EstimateNo public source provides a precise figure. This is our estimate. | 7% | 28% | +2 |
Prepare or serve menu items, such as sandwiches or salads.EstimateNo public source provides a precise figure. This is our estimate. | 7% | 28% | +2 |
Clean or sanitize work areas, utensils, or equipment.EstimateNo public source provides a precise figure. This is our estimate. | 6% | 28% | +1.7 |
Clean service or seating areas.EstimateNo public source provides a precise figure. This is our estimate. | 6% | 28% | +1.7 |
Other less frequent tasks (weight estimated from frequency).EstimateNo public source provides a precise figure. This is our estimate. | 42% | 28% | +11.8 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- comply with food safety and hygiene
- educate customers on tea varieties
- maintain relationship with customers
- greet guests
- handle customer complaints
- upsell products
- handover the service area
- set up the coffee area
- maintain customer service
- work in a hospitality team
Sources for this job· O*NET 35-3023.01· ESCO/ISCO 5132