As of August 2026, Automotive Engineering Technicians has an AI-exposure score of 60/100 (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: This role starts from O*NET 29.1 occupational descriptors and is empirically grounded by Penn/OpenAI GPTs are GPTs study, Anthropic Economic Index (June 26, 2026), Felten, Raj and Seamans AIOE index. BLS labor-market figures are separate context, not score inputs.

AI Exposure Score for

Automotive Engineering Technicians

60/100
Elevated exposure
LowModerateElevatedHighVery High

More exposed than 64% of the roles we track. Median pay ~US$74,510. About 3,200 projected openings a year (BLS 2024–34 - growth plus replacement).

Pay & demand figures are US medians (BLS, in USD) - your local figures will differ. Your exposure score applies broadly.

Will AI replace Automotive Engineering Technicians?

No exposure score can predict whether AI will replace this role. The 60/100 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.

Data sources, not endorsements: O*NET and curated role data; empirical AI signals where matched; BLS employment, pay, and demand as separate context.Methodology Data sources
Role snapshot

What this role usually involves

O*NET 29.1 exact occupation

Assist engineers in determining the practicality of proposed product design changes and plan and carry out tests on experimental test devices or equipment for performance, durability, or efficiency.

Common titles
Laboratory Technician (Lab Technician)Research Technician
O*NET job-zone preparation
Job Zone 3 · Medium Preparation Needed Most occupations in this zone require training in vocational schools, related on-the-job experience, or an associate's degree. Previous work-related skill, knowledge, or experience is required for these occupations.

Broad guidance for this preparation level; exact requirements vary by role and employer.

Skills and knowledge
Reading ComprehensionCritical ThinkingSpeakingComplex Problem SolvingWritingMathematics
Work context
Indoor controlled settingFrequent contact with othersDecision latitudeRepeating tasksConsequence of error

Source: O*NET 29.1 exact occupation - Automotive Engineering Technicians, SOC 17-3027.01. Context describes the role; the AI-exposure score remains a separate task-exposure estimate.

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Automotive Engineering Technicians tasks, by AI exposure

Automatable
  • Set up mechanical, hydraulic, or electric test equipment in accordance with engineering specifications, standards, or test procedures.
  • Maintain test equipment in operational condition by performing routine maintenance or making minor repairs or adjustments as needed.
  • Install equipment, such as instrumentation, test equipment, engines, or aftermarket products, to ensure proper interfaces.
Augmentable
  • Document test results, using cameras, spreadsheets, documents, or other tools.
  • Read and interpret blueprints, schematics, work specifications, drawings, or charts.
13 more augmentable tasks locked in the report.
Durable

No durable tasks identified for this role - its individually-assessed tasks split 83% augmentable / 17% automatable.

We analyzed all 18 Automotive Engineering Technicians tasks - 3 automatable and 15 augmentable. The full task map - every task with exactly what to do about each - is in your Career Report.

Your report unlocks three concrete artifacts

Full task map

Every task scored with what to automate, augment, or protect.

Adjacent path comparison

Related roles with exposure deltas, salary, demand, and reachability. Lower-exposure options appear only when the data supports them.

30/60/90 plan + PDF

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

Relatedness shows how reachable a move may be. A path is labeled lower exposure only when its score is at least 6 points lower; every row shows the measured difference.

Automotive Service Technicians and Mechanics
48% skills overlap; 20 points lower - lower exposure; Moderate band; ~US$50,620
View path
40
7 more adjacent paths with exposure deltas, salary, demand, and reachability in your Career Report.
Agent Reality Check (on-site workflow preview)

Set up mechanical, hydraulic, or electric test equipment in accordance with engineering specifications, standards, or test procedures.

Check what deployed AI agents can attempt on tasks like this using dated capability evidence. You review the real task before any analysis runs.
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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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Important: This is an estimate of AI exposure, not a prediction that your job will disappear. It is designed to help you understand how your role may change and improve your career resilience.

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Automotive Engineering Technicians - median pay by US state (BLS OEWS, USD)

California: US$87,390New York: US$78,210Texas: US$76,610Florida: US$59,720

Median annual wage, in USD. US national: US$74,510. More states are being added.

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