As of August 2026,
Machine Learning Engineer has an AI-exposure score of 54/100
(Elevated exposure) on the AI-Safe Careers index. This is an estimate of task
exposure, not a prediction of job loss.
Score inputs for this role:
AI-Safe Careers curated modeled estimate. This curated role has no matched O*NET SOC or empirical occupation signal. Treat it as a lower-confidence directional estimate. BLS labor-market figures are separate context, not score inputs.
Machine Learning Engineer
More exposed than 42% of the roles we track. Median pay ~US$140,000.
Pay & demand figures are US medians (in USD; curated approximation (not refreshed from BLS; 2022–32 framing)) - your local figures will differ. Your exposure score applies broadly.
Will AI replace Machine Learning Engineer?
No exposure score can predict whether AI will replace this role. The 54/100 score means our current model estimates elevated task exposure from the sources listed on this page. It does not predict an employer decision, headcount, or an individual outcome. The task map below shows the work assessed and where human judgment remains important.
Early-career context (study ages 22-25)
This role is in the Elevated exposure band, but its assessed task mix is not automation-heavy. The study found the decline concentrated where AI was more likely to automate rather than augment work, so the headline figure should not be applied directly.
The November 2025 revision reports a 16% relative employment decline for workers ages 22-25 in the most AI-exposed U.S. occupations, after firm-level controls, relative to workers in less-exposed fields and more experienced workers in the same occupations.
Use the task map to identify durable work and the skills worth building early.
Stanford Digital Economy Lab, Canaries in the Coal Mine - November 2025 revision
This is group-level U.S. payroll evidence, not a personal forecast. The authors do not claim that AI alone caused the change, and this context does not alter the exposure score.
How this role compares to similar Technology roles
What this role usually involves
Develop and implement a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software. Apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from large structured and unstructured datasets. Visualize, interpret, and report data findings. May create dynamic data reports.
Broad guidance for this preparation level; exact requirements vary by role and employer.
Source: O*NET 29.1 closest reviewed source - Data Scientists, SOC 15-2051.00. Context describes the role; the AI-exposure score remains a separate task-exposure estimate.
Machine Learning Engineer tasks, by AI exposure
O*NET reports Machine Learning Engineer under Data Scientists and related computing occupations. The reviewed Data Scientists subset deliberately includes model feature selection, comparison, validation, data processing, programming, and analytic problem framing; those same ML-specific tasks are excluded from the general Data Analyst bridge on occupational-relevance grounds. How we map tasks →
- Identify business problems or management objectives that can be addressed through data analysis. O*NET: Data Scientists
- Clean and manipulate raw data using statistical software. O*NET: Data Scientists
- Train and deploy models
- Build data pipelines
- Frame ML problems
- Evaluate model trade-offs
We analyzed all 15 Machine Learning Engineer tasks - 2 automatable, 6 augmentable and 7 durable. The full task map - every task with exactly what to do about each - is in your Career Report.
Your report unlocks three concrete artifacts
Every task scored with what to automate, augment, or protect.
Related roles with exposure deltas, salary, demand, and reachability. Lower-exposure options appear only when the data supports them.
A keepable roadmap plus resume and LinkedIn repositioning.
Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate - not generic advice.
Adjacent career paths
No evidence-backed lower-exposure match appears in this O*NET adjacency set. The adjacent paths below remain useful comparisons; the strongest resilience moves are task-level.
Identify business problems or management objectives that can be addressed through data analysis.
Your AI-Safe Career Report
Every task scored with what to do about it; adjacent paths with honest exposure deltas, salary, demand, and reachability; a skill-gap map; a 30/60/90-day roadmap; Agent Reality Check; plus a résumé and LinkedIn rewrite and professional PDF.
Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate.
Workers with AI skills earn a roughly 62% wage premium - adapting pays. - PwC Global AI Jobs Barometer, 2026
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