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?
Risk manager
29%
#60 / 623
View rankingRisk manager
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 |
|---|---|---|---|
Maps, models and risk reporting.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 52% | +11.4 |
Policies, limits and stress tests.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 45% | +8.1 |
Escalate an incident and talk to desks.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Committee, exceptions and negotiation.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Data quality and model assumptions.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
Stop the risk and sign the opinion.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Maps, models and risk reporting
- Policies, limits and stress tests
- Escalate an incident and talk to desks
- Committee, exceptions and negotiation
- Data quality and model assumptions
- Stop the risk and sign the opinion
- risk manager
- gestor de riesgos
- riesgo operacional
Sources for this job· O*NET 13-2099.02· ESCO/ISCO 2413