제조 엔지니어
건축·공학직Manufacturing Engineers
- O*NET-SOC 코드
- 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.
한국어 직업명은 AI Changing Work의 번역입니다. 과업 문장과 원천 값은 공표된 영문 그대로 표시합니다.
AI 노출도
- 데이터 출처: BLS발행 시점: 2026-08
매우 높음· 상대
낮음4단계 상대 범주매우 높음직업군 단위 값
척도 · 모수 · 출처
4단계 상대 범주 (낮음 / 보통 / 높음 / 매우 높음)
BLS 고용전망 표의 세부직업 831개 기준. 값은 NEM(전국고용매트릭스) 코드 단위로 부여되므로, 같은 NEM 코드를 쓰는 직업은 같은 밴드를 받습니다
- 데이터 출처: Anthropic발행 시점: 2026-03
0.037
0.000여기 실린 값의 범위0.745직업군 단위 값
척도 · 모수 · 출처
관측 노출 지수, 발행된 그대로 0–1
O*NET 과업 매핑 기준
SOC 2018 직업 단위로 발행된 값이며, 같은 SOC 2018 코드를 가진 O*NET 직업은 모두 이 값을 받습니다
- 데이터 출처: ILO발행 시점: 2025
0.37
0.09여기 실린 값의 범위0.70직업군 단위 값
척도 · 모수 · 출처
생성형 AI 노출 지수, 발행된 그대로 0–1
ISCO-08 직업군 단위로 발행된 값입니다. 미국 노동통계국(BLS)의 공식 교차표(ISCO-08→2010 SOC, 2010 SOC→2018 SOC)를 그대로 적용해 이 직업에 연결했으며, 연결은 전부 또는 일부 대응입니다
이 원천이 어떤 종류의 값인가
BLS 범주는 절대 수준이 아니라 상대 순위이며, 1차 측정도 아닙니다. 여러 선행 연구가 매긴 직업별 백분위 순위를 4단계로 묶은 값입니다. 고용·임금 예측도, 도입 확률도 아니며 자동화와 증강을 구분하지 않습니다.
AI 노출도 (OpenAI 루브릭)
평가 과업 24개 · β ≥ 0.5 인 과업 22개 (91.7%)
β = 직접 노출(E1) + 0.5 × 도구 활용 시 노출(E2). 원본 저장소의 정의를 따릅니다.
- 원천 단위: O*NET 27.2 과업 → O*NET 31.0 직업 코드
- 채점된 과업이 모두 O*NET 31.0 과업 목록에 있습니다.
- 출처
- OpenAI "GPTs are GPTs" exposure rubric
- 판
- gh-main-0471612
- 라이선스
- MIT License, Copyright (c) 2024 OpenAI
과업
O*NET® 31.0 Database의 과업 문장입니다. 핵심(Core) 과업을 먼저 보입니다.
| 과업 | 유형 | β (OpenAI) |
|---|---|---|
| Apply continuous improvement methods, such as lean manufacturing, to enhance manufacturing quality, reliability, or cost-effectiveness. | 핵심 | 0.5 |
| Design layout of equipment or workspaces to achieve maximum efficiency. | 핵심 | 0.5 |
| Communicate manufacturing capabilities, production schedules, or other information to facilitate production processes. | 핵심 | 0.5 |
| Design, install, or troubleshoot manufacturing equipment. | 핵심 | 0.5 |
| Estimate costs, production times, or staffing requirements for new designs. | 핵심 | 0.5 |
| Evaluate manufactured products according to specifications and quality standards. | 핵심 | 0.5 |
| Investigate or resolve operational problems, such as material use variances or bottlenecks. | 핵심 | 0.5 |
| Prepare documentation for new manufacturing processes or engineering procedures. | 핵심 | 1 |
| Purchase equipment, materials, or parts. | 핵심 | 0.5 |
| Review product designs for manufacturability or completeness. | 핵심 | 0.5 |
| Troubleshoot new or existing product problems involving designs, materials, or processes. | 핵심 | 0.5 |
| Prepare reports summarizing information or trends related to manufacturing performance. | 핵심 | 0.5 |
| Provide technical expertise or support related to manufacturing. | 핵심 | 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. | 핵심 | 0.5 |
| Supervise technicians, technologists, analysts, administrative staff, or other engineers. | 핵심 | 0 |
| Train production personnel in new or existing methods. | 핵심 | 0 |
| Analyze the financial impacts of sustainable manufacturing processes or sustainable product manufacturing. | 핵심 | 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. | 핵심 | 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. | 핵심 | 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. | 핵심 | 0.5 |
| Incorporate new manufacturing methods or processes to improve existing operations. | 핵심 | 0.5 |
| Determine root causes of failures or recommend changes in designs, tolerances, or processing methods, using statistical procedures. | 핵심 | 0.5 |
| Design tests of finished products or process capabilities to establish standards or validate process requirements. | 핵심 | 0.5 |
| Redesign packaging for manufactured products to minimize raw material use or waste. | 보조 | 0.5 |
직업 정보
출처와 귀속
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.