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?
Taxi driver
18%
#570 / 623
View rankingTaxi driver
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 |
|---|---|---|---|
Navigate, take payment and log shifts.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Find the passenger and read the city.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Drive safely in all kinds of traffic.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Care, luggage and passenger languages.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Car maintenance and shifts.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Licences, radio and zone knowledge.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 22% | +3.1 |
| Total | 100% | 19% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- drive
- route
- load goods
- Navigate, take payment and log shifts
- Find the passenger and read the city
- Drive safely in all kinds of traffic
- Care, luggage and passenger languages
- Car maintenance and shifts
- Licences, radio and zone knowledge
- taxista
Sources for this job· O*NET 53-3054.00· ESCO/ISCO 8322