Automation index
How much of a Site reliability engineer’s job can AI already do?
A runbook and autoscale come earlier. The 3 a.m. incident without taking production down do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Site reliability engineer
28%
AI can already do 28% of this job.
#103 / 623
View rankingSite reliability 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 |
|---|---|---|---|
SLOs, alerts and the runbook.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Incident, war room and not making it worse.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
CI/CD, capacity and the error budget.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Postmortem, toil and automating the repeated.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The product that wants to ship and the SLO that will not allow it.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Not taking production down, and signing the change.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- SLOs
- runbooks
- handle incidents
- error budget
- autoscale
- postmortem
- reduce toil
- reliability CI/CD
Sources for this job· O*NET 15-1244.00· ESCO/ISCO 2512
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