Automotive Engineers
Architecture and Engineering Occupations- O*NET-SOC 代码
- 17-2141.02
Develop new or improved designs for vehicle structural members, engines, transmissions, or other vehicle systems, using computer-assisted design technology. Direct building, modification, or testing of vehicle or components.
职业名称和任务陈述按其发布时的英文原文显示。标签（包括任务类型）为译文。
AI暴露度
- 数据来源: BLS发布时间: 2026-08
很高· 相对
低四级相对区间很高职业群单位数值
尺度 · 母数 · 来源
四级相对类别（低 / 中等 / 高 / 很高）
以BLS就业预测表中831个细分职业为基数。取值按 NEM（全国就业矩阵）代码给定，因此共用同一 NEM 代码的职业得到相同分档
- 数据来源: Anthropic发布时间: 2026-03
0.081
0.000此处所载数值的范围0.745职业群单位数值
尺度 · 母数 · 来源
- 数据来源: ILO发布时间: 2025
0.32
0.09此处所载数值的范围0.70职业群单位数值
尺度 · 母数 · 来源
生成式AI暴露度指数，按发布原值0–1
按 ISCO-08 细类（4 位）发布的数值。按原样应用美国劳工统计局（BLS）的官方对照表（ISCO-08→2010 SOC、2010 SOC→2018 SOC）关联到本职业，对应关系为全部或部分对应
该来源发布的是什么性质的数值
BLS的类别是相对排位而非绝对水平，也不是一手测量：它把多项已发表研究给出的职业百分位排名归并为四个等级。它不是就业或工资预测，不是采用概率，也不区分自动化与增强。
AI 暴露度（OpenAI 评分标准）
已评分任务 25 项 · β ≥ 0.5 的任务 24 项（96.0%）
β = 直接暴露（E1）+ 0.5 × 借助工具时的暴露（E2），依据原始代码库的定义。
- 原始单位：O*NET 27.2 任务 → O*NET 31.0 职业代码
- 所有已评分任务均在 O*NET 31.0 任务列表中。
- 来源
- OpenAI "GPTs are GPTs" exposure rubric
- 版本
- gh-main-0471612
- 许可
- MIT License, Copyright (c) 2024 OpenAI
任务
来自 O*NET® 31.0 Database 的任务陈述，核心任务在前。
| 任务 | 类型 | β（OpenAI） |
|---|---|---|
| Read current literature, attend meetings or conferences, or talk with colleagues to stay abreast of new automotive technology or competitive products. | 核心 | 0.5 |
| Establish production or quality control standards. | 核心 | 0.5 |
| Prepare or present technical or project status reports. | 核心 | 1 |
| Develop or implement operating methods or procedures. | 核心 | 0.5 |
| Write, review, or maintain engineering documentation. | 核心 | 1 |
| Conduct research studies to develop new concepts in the field of automotive engineering. | 核心 | 0.5 |
| Coordinate production activities with other functional units, such as procurement, maintenance, or quality control. | 核心 | 0.5 |
| Provide technical direction to other engineers or engineering support personnel. | 核心 | 1 |
| Perform failure, variation, or root cause analyses. | 核心 | 0.5 |
| Develop or integrate control feature requirements. | 核心 | 1 |
| Develop engineering specifications or cost estimates for automotive design concepts. | 核心 | 0.5 |
| Develop calibration methodologies, test methodologies, or tools. | 核心 | 1 |
| Conduct automotive design reviews. | 核心 | 0.5 |
| Calibrate vehicle systems, including control algorithms or other software systems. | 核心 | 1 |
| Build models for algorithm or control feature verification testing. | 核心 | 1 |
| Alter or modify designs to obtain specified functional or operational performance. | 核心 | 0.5 |
| Design or analyze automobile systems in areas such as aerodynamics, alternate fuels, ergonomics, hybrid power, brakes, transmissions, steering, calibration, safety, or diagnostics. | 核心 | 0.5 |
| Conduct or direct system-level automotive testing. | 核心 | 0 |
| Design control systems or algorithms for purposes such as automotive energy management, emissions management, or increased operational safety or performance. | 核心 | 1 |
| Create design alternatives for vehicle components, such as camless or dual-clutch engines or alternative air-conditioning systems, to increase fuel efficiency. | 核心 | 0.5 |
| Design vehicles for increased recyclability or use of natural, renewable, or recycled materials in vehicle construction. | 核心 | 0.5 |
| Design vehicles that use lighter materials, such as aluminum, magnesium alloy, or plastic, to improve fuel efficiency. | 核心 | 0.5 |
| Develop specifications for vehicles powered by alternative fuels or alternative power methods. | 核心 | 0.5 |
| Research computerized automotive applications, such as telemetrics, intelligent transportation systems, artificial intelligence, or automatic control. | 核心 | 1 |
| Research or implement green automotive technologies involving alternative fuels, electric or hybrid cars, or lighter or more fuel-efficient vehicles. | 核心 | 0.5 |
职业信息
来源与署名
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: 17-2141.02 Automotive 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.