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?
Tow-truck driver
20%
#439 / 623
View rankingTow-truck 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 |
|---|---|---|---|
Call, route and the vehicle type.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Hard shoulder, hook and traffic that will not stop.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Hydraulics, bed and the low car that scrapes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Garage, the note and the client who argues.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Night, rain and not becoming the next crash.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Not making the crash worse, and signing the hook.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 20% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- hook vehicles
- hard shoulder
- the bed
- recovery calls
- crane hydraulics
- recovery notes
- traffic
- low cars
Sources for this job· O*NET 53-3032.00· ESCO/ISCO 8332