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 / 703
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?
Instructional designer
27%
#158 / 703
View rankingInstructional designer
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 |
|---|---|---|---|
Aim, the storyboard and the module.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Pilot, the learner and what is not understood.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Activity, the feedback and not putting a silly test.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
LMS, the figure and not inflating completion.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The client, the deadline and not cutting the practice.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That they learn, and not leaving the empty video.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- course storyboard
- modules
- learner pilot
- activities
- LMS
- completion
- feedback
- not a silly test
Sources for this job· O*NET 25-9031.00· ESCO/ISCO 2351