Automation index
How much of a Calibration technician’s job can AI already do?
A certificate comes earlier. The instrument measuring without signing blind do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Calibration technician
22%
AI can already do 22% of this job.
#383 / 633
View rankingCalibration technician
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 |
|---|---|---|---|
Standard, uncertainty and the certificate.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Instrument, the adjust and the one that will not go.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Field, the one that cannot move and the portable standard.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Lab, the temperature and not skipping the soak.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The client, the rush and not signing blind.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
That it measures, and a true certificate.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- standards
- uncertainty
- calibration certificates
- adjust
- instruments
- thermal soak
- metrology lab
- the field
Sources for this job· O*NET 17-3028.00· ESCO/ISCO 3111
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