LO
Machine learning engineer — how we know
The page itself gives the judgements. This one gives what they rest on: which technologies bear on the work, how the estimate moved since language models reached the public, and the method behind both.
Which technologies matter here#
Four separate signals. They are deliberately not added together — a job exposed to two technologies is not twice as exposed.
How it got here#
The index is not a static number. This is where it would have sat at each capability checkpoint since ChatGPT — reconstructed, and labelled as such.
—— this stretch contains a verified event- - - no event in this stretch — reconstruction only0 = no task exposed, 100 = every task exposed
● 3 verified events for this occupation, plotted at the date it happened — the parts of the line near a marker are anchored to something checkable.
The only curve on this site that rises fast and then comes back down, and both halves have a named mechanism. It starts high because hand-designed features were already displaced before this period began — representation learning did that through the 2010s, not generative AI. The sharp 2023-2024 climb is not a machine learning to do this job: it is a large share of problems acquiring a purchasable substitute, so the bespoke artefact stopped being necessary. The fall from 2025 is the other half of the same move — once the model is bought, what is left is judging whether it works on your problem, and that got harder as the systems got more capable, because a plausible wrong answer is harder to catch than an obviously wrong one. Read the peak as the moment the classical task set was cheapest to replace, not as a maximum this occupation is heading back toward.
A flat line is not a forecast of safety. It says which tasks automation has reached so far — the occupations that moved least here are the ones where the constraint is physical or regulatory, and both of those can change.
Method and sources#
- Assessment date
- 2026-09-12
- Basis of the task judgements
- 2 evidence-backed · 4 platform inference · 0 not enough evidence
- Verified events
- 3