Automation index
How much of a Nuclear engineer’s job can AI already do?
A core model comes earlier. Running the plant and not playing with reactivity do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Nuclear engineer
24%
AI can already do 24% of this job.
#267 / 633
View rankingNuclear 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 |
|---|---|---|---|
Neutronics, thermal-hydraulics and the file.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 44% | +7 |
Control room, tests and the set-point.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Licence, the regulator and the safety analyses.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Outage, reload and spent fuel.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
The event, safety culture and the report.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Not playing with reactivity, and signing the analysis.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 24% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- neutronics
- thermal-hydraulics
- nuclear licence
- reload
- safety analysis
- control room
- spent fuel
- safety culture
Sources for this job· O*NET 17-2161.00· ESCO/ISCO 2149
IS WORK OVER?
ML engineer
29%
#64 / 633
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