Manufacturing Engineers
Architecture and Engineering Occupations- O*NET-SOC code
- 17-2112.03
Design, integrate, or improve manufacturing systems or related processes. May work with commercial or industrial designers to refine product designs to increase producibility and decrease costs.
AI exposure
- Data source: BLSPublished: 2026-08
Very high· relative
LowFour relative bandsVery highGroup-level value
Scale, basis and source
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
- Data source: AnthropicPublished: 2026-03
0.037
0.000Range of values carried here0.745Group-level value
Scale, basis and source
Observed exposure index, 0–1 as published
Mapped onto O*NET tasks
Published per SOC 2018 occupation; every O*NET occupation with the same SOC 2018 code carries this value
- Data source: ILOPublished: 2025
0.37
0.09Range of values carried here0.70Group-level value
Scale, basis and source
Generative AI exposure index, 0–1 as published
Published per ISCO-08 unit group. Linked to this occupation, wholly or in part, by applying U.S. Bureau of Labor Statistics crosswalks (ISCO-08 to 2010 SOC, 2010 SOC to 2018 SOC) as published
What kind of figure this source publishes
The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.
AI exposure (OpenAI rubric)
24 rated tasks · 22 tasks with β ≥ 0.5 (91.7%)
β = direct exposure (E1) + 0.5 × exposure when tools are available (E2), per the source repository's definition.
- Source unit: O*NET 27.2 tasks → O*NET 31.0 occupation code
- All rated tasks are in the O*NET 31.0 task list.
- Source
- OpenAI "GPTs are GPTs" exposure rubric
- Release
- gh-main-0471612
- License
- MIT License, Copyright (c) 2024 OpenAI
Tasks
Task statements from the O*NET® 31.0 Database, core tasks first.
| Task | Type | β (OpenAI) |
|---|---|---|
| Apply continuous improvement methods, such as lean manufacturing, to enhance manufacturing quality, reliability, or cost-effectiveness. | Core | 0.5 |
| Design layout of equipment or workspaces to achieve maximum efficiency. | Core | 0.5 |
| Communicate manufacturing capabilities, production schedules, or other information to facilitate production processes. | Core | 0.5 |
| Design, install, or troubleshoot manufacturing equipment. | Core | 0.5 |
| Estimate costs, production times, or staffing requirements for new designs. | Core | 0.5 |
| Evaluate manufactured products according to specifications and quality standards. | Core | 0.5 |
| Investigate or resolve operational problems, such as material use variances or bottlenecks. | Core | 0.5 |
| Prepare documentation for new manufacturing processes or engineering procedures. | Core | 1 |
| Purchase equipment, materials, or parts. | Core | 0.5 |
| Review product designs for manufacturability or completeness. | Core | 0.5 |
| Troubleshoot new or existing product problems involving designs, materials, or processes. | Core | 0.5 |
| Prepare reports summarizing information or trends related to manufacturing performance. | Core | 0.5 |
| Provide technical expertise or support related to manufacturing. | Core | 0.5 |
| Read current literature, talk with colleagues, participate in educational programs, attend meetings or workshops, or participate in professional organizations or conferences to keep abreast of developments in the manufacturing field. | Core | 0.5 |
| Supervise technicians, technologists, analysts, administrative staff, or other engineers. | Core | 0 |
| Train production personnel in new or existing methods. | Core | 0 |
| Analyze the financial impacts of sustainable manufacturing processes or sustainable product manufacturing. | Core | 0.5 |
| Develop sustainable manufacturing technologies to reduce greenhouse gas emissions, minimize raw material use, replace toxic materials with non-toxic materials, replace non-renewable materials with renewable materials, or reduce waste. | Core | 0.5 |
| Evaluate current or proposed manufacturing processes or practices for environmental sustainability, considering factors such as greenhouse gas emissions, air pollution, water pollution, energy use, or waste creation. | Core | 0.5 |
| Identify opportunities or implement changes to improve manufacturing processes or products or to reduce costs, using knowledge of fabrication processes, tooling and production equipment, assembly methods, quality control standards, or product design, materials and parts. | Core | 0.5 |
| Incorporate new manufacturing methods or processes to improve existing operations. | Core | 0.5 |
| Determine root causes of failures or recommend changes in designs, tolerances, or processing methods, using statistical procedures. | Core | 0.5 |
| Design tests of finished products or process capabilities to establish standards or validate process requirements. | Core | 0.5 |
| Redesign packaging for manufactured products to minimize raw material use or waste. | Supplemental | 0.5 |
Occupation information
Sources and attribution
This page includes information from the O*NET® 31.0 Database (https://www.onetcenter.org/database.html) by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). O*NET® is a trademark of USDOL/ETA. AI Changing Work has modified all or some of this information: the O*NET-SOC code, title and task statements are reproduced in English without change; task-type labels are shown in the page's language and tasks are listed core first; any Korean occupation title shown on the Korean-language page is AI Changing Work's translation; any KSCO-8 unit groups linked to this occupation were paired with it by AI Changing Work's judgment, and the relation labels and statuses are AI Changing Work's additions. USDOL/ETA has not approved, endorsed, or tested these modifications.
Any AI exposure figures on this page are published by third parties, not by AI Changing Work, and none is part of the O*NET information. OpenAI publishes task-level scores (MIT License) for O*NET 27.2 task statements; each is shown next to the O*NET 31.0 task statement with the same task ID, whose wording can differ from the 27.2 statement that was scored. OpenAI also publishes occupation-level scores for O*NET-SOC codes in the same release, and any such score is shown on the O*NET occupation with the same code. Anthropic publishes an observed exposure index in the Anthropic Economic Index (CC-BY), and the U.S. Bureau of Labor Statistics publishes relative AI exposure categories (public domain); both are published per SOC code, and each value is shown on every O*NET occupation with that code. The International Labour Organization publishes a generative AI exposure index in ILO Working Paper 140 (CC BY 4.0) for ISCO-08 unit groups; AI Changing Work links those groups to O*NET occupations by applying the U.S. Bureau of Labor Statistics ISCO-08 to 2010 SOC and 2010 SOC to 2018 SOC crosswalks as published, without case-by-case selection, and these crosswalks match many groups only in part. Where several unit groups are linked, each group's published value is listed, and any summary shows only the lowest and highest of those values with the number of groups; no exposure figure is averaged or recalculated. Any employment figures are published by the U.S. Bureau of Labor Statistics for the SOC group containing this occupation. Each source is credited where its figures are shown.
O*NET OnLine: 17-2112.03 Manufacturing Engineers
KSCO 코드·명칭: 한국표준직업분류(제8차 개정) — 통계청 고시 제2024-328호 (2024-07-01 고시, 2025-01-01 시행). 저작권법 제7조 제2호의 고시 항목이다. 명칭 표기(가운뎃점·띄어쓰기)는 해설서 2차 정오 반영판의 표기를 따랐으며, 고시 항목표와는 18개 명칭에서 가운뎃점 글리프나 띄어쓰기만 다르다. 통계청은 2025년 10월 국가데이터처로 개편되었다. 이 페이지의 KSCO 연결은 통계청·국가데이터처의 공식 연계표가 아니다.