IS WORK OVER?

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

ML 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.

TaskShareAIPoints
Train models, features and notebooks.
Estimate

No public source provides a precise figure. This is our estimate.

22%48%+10.6
Evaluate, document and compare runs.
Estimate

No public source provides a precise figure. This is our estimate.

18%42%+7.6
Deploy, monitor and retrain.
Estimate

No public source provides a precise figure. This is our estimate.

16%22%+3.5
Talk to product about what a good error is.
Estimate

No public source provides a precise figure. This is our estimate.

16%15%+2.4
Dirty data, bias and model limits.
Estimate

No public source provides a precise figure. This is our estimate.

14%18%+2.5
On-call, incidents and rollback.
Estimate

No public source provides a precise figure. This is our estimate.

14%16%+2.2
Total100%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

University tutor

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.

TaskShareAIPoints
Form, the file and the one who does not come.
Estimate

No public source provides a precise figure. This is our estimate.

18%48%+8.6
Mark, the cut and the one who gives up.
Estimate

No public source provides a precise figure. This is our estimate.

16%40%+6.4
Grant, the paper and not letting it lapse.
Estimate

No public source provides a precise figure. This is our estimate.

16%22%+3.5
Health, the referral and not playing clinician.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
The dean, the dropout figure and not dressing it.
Estimate

No public source provides a precise figure. This is our estimate.

18%14%+2.5
That they ask for help, and not dropping out.
Estimate

No public source provides a precise figure. This is our estimate.

16%12%+1.9
Total100%27%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • university tutoring
  • degree dropout
  • grants
  • files
  • health referral
  • the dean
  • grade cuts
  • tutoring forms

Sources for this job· O*NET 25-1199.00· ESCO/ISCO 2310

Analyse my job

Jobs with similar scores

  1. 61Court case manager29%
  2. 62Labour consultant29%
  3. 63Quality engineer29%
  4. 65Occupational physician29%
  5. 66Career counsellor29%
  6. 67Construction laborer29%