Automation index
How much of a Robotics engineer’s job can AI already do?
A path and a twin come earlier. Putting the robot on the line without taking anyone with it do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Robotics engineer
25%
AI can already do 25% of this job.
#234 / 623
View rankingRobotics 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 |
|---|---|---|---|
Paths, PLC and the cell twin.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 48% | +7.7 |
Cell, grippers and the cycle that will not close.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 16% | +2.9 |
Safety, fencing and CE validation.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Maintenance, backups and the overwritten program.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Production, takt and the robot that stops.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That it takes nobody with it, and signing the cell.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- program robots
- paths
- robot cells
- safety fencing
- cell PLC
- CE validation
- grippers
- takt
Sources for this job· O*NET 17-2199.00· ESCO/ISCO 2149
IS WORK OVER?
ML engineer
29%
#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