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?
Product owner
27%
#171 / 623
View rankingProduct owner
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 |
|---|---|---|---|
Write stories, criteria and backlog.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 50% | +11 |
Maps, roadmaps and release notes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Refine with engineering and design.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Talk to users and stakeholders.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Prioritise, cut scope and say no.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Accept the increment and own the value.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Write stories, criteria and backlog
- Maps, roadmaps and release notes
- Refine with engineering and design
- Talk to users and stakeholders
- Prioritise, cut scope and say no
- Accept the increment and own the value
- product owner
- po agil
Sources for this job· O*NET 15-1199.09· ESCO/ISCO 2519