Occupation · SOC 17-2112.01
Design objects, facilities, and environments to optimize human well-being and overall system performance, applying theory, principles, and data regarding the relationship between humans and respective technology. Investigate and analyze characteristics of human behavior and performance as it relates to the use of technology.
Also called: Engineer · Ergonomist · Human Factors Engineer · Occupational Ergonomist · Board Certified Ergonomist · Certified Professional Ergonomist · Cognitive Engineer · Ergonomic Consultant · Ergonomics Technical Advisor · Human Factors Advisor · Engineering Psychologist · Ergonomic Specialist
Job family: Architecture and Engineering Occupations
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-17-2112-01/context.md directly.
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Task areas where people work with AI — iterating, learning, or checking — staying in the loop rather than handing the task off.
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Independent published positions, read together — not a forecast.
81st-percentile task overlap — yet about 25,200 openings a year (+11% projected, BLS), and observed AI use leans 5720% copilot, not hand-off (AEI) . What exposure means →
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) High | 80th | 1.1 | |
| LLM task exposure, γ (OpenAI / Eloundou) High | 75th | 0.9 | |
| AI assistant applicability (Microsoft) High | 81st | 0.3 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.5), and including AI-powered software (γ 0.9). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
Most of this job's tasks can be done remotely (Dingel–Neiman), which tends to track with higher digital and AI exposure.
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.0 · 18th percentile among occupations · Low
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Investigate theoretical or conceptual issues, such as the human design considerations of lunar landers or habitats. | 5.1% | |
| Analyze complex systems to determine potential for further development, production, interoperability, compatibility, or usefulness in a particular area, such as aviation. | 1.1% | |
| Perform statistical analyses, such as social network pattern analysis, network modeling, discrete event simulation, agent-based modeling, statistical natural language processing, computational sociology, mathematical optimization, or systems dynamics. | 0.5% | |
| Apply modeling or quantitative analysis to forecast events, such as human decisions or behaviors, the structure or processes of organizations, or the attitudes or actions of human groups. | 0.3% | |
| Establish system operating or training requirements to ensure optimized human-machine interfaces. | 0.2% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Growing fast · +11.0% by 2034 |
| Projected annual openings | 25,200 |
| Employment 2024 → 2034 | 351,100 → 389,600 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Industrial and Production Engineers · 2141 | 37% | Minimal |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Augmentation vs. automation | 57.2% working with AI · 9.6% handed to AI |
| Most common way people use AI here | Learning · you ask AI to explain or teach |
| Typical AI autonomy | 4.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 22.4% |
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Investigate theoretical or conceptual issues, such as the human design considerations of lunar landers or habitats. | Learning | 1.5% |
| Operate testing equipment, such as heat stress meters, octave band analyzers, motion analysis equipment, inclinometers, light meters, velometers, sling psychrometers, or colormetric detection tubes. | Learning | 0.6% |
| Analyze complex systems to determine potential for further development, production, interoperability, compatibility, or usefulness in a particular area, such as aviation. | — | 0.3% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Operate testing equipment, such as heat stress meters, octave band analyzers, motion analysis equipment, inclinometers, light meters, velometers, sling psychrometers, or colormetric detection tubes. | 90.3% | |
| Investigate theoretical or conceptual issues, such as the human design considerations of lunar landers or habitats. | 89.6% | |
| Analyze complex systems to determine potential for further development, production, interoperability, compatibility, or usefulness in a particular area, such as aviation. | 88.2% |
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
Help me investigate theoretical or conceptual issues, such as the human design considerations of lunar landers or habitats. From: Investigate theoretical or conceptual issues, such as the human design considerations of lunar landers or habitats. · 1.5% of measured AI use · learning
Help me operate testing equipment, such as heat stress meters, octave band analyzers, motion analysis equipment, inclinometers, light meters, velometers, sling psychrometers, or colormetric detection tubes. From: Operate testing equipment, such as heat stress meters, octave band analyzers, motion analysis equipment, inclinometers, light meters, velometers, sling psychrometers, or colormetric detection tubes. · 0.6% of measured AI use · learning
Help me analyze complex systems to determine potential for further development, production, interoperability, compatibility, or usefulness in a particular area, such as aviation. From: Analyze complex systems to determine potential for further development, production, interoperability, compatibility, or usefulness in a particular area, such as aviation. · 0.3% of measured AI use
All 26 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
Newer responsibilities O*NET has flagged as growing for this occupation.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| Psychology | 4.2 | |
| English Language | 4.0 | |
| Design | 3.9 | |
| Engineering and Technology | 3.8 | |
| Education and Training | 3.6 | |
| Mathematics | 3.5 | |
| Customer and Personal Service | 3.5 |
| Reading Comprehension | 4.0 | |
| Active Listening | 4.0 | |
| Writing | 4.0 | |
| Speaking | 4.0 | |
| Critical Thinking | 4.0 | |
| Active Learning | 3.8 | |
| Mathematics | 3.5 | |
| Monitoring | 3.5 | |
| Science | 3.3 | |
| Learning Strategies | 3.3 |
| Complex Problem Solving | 4.0 | |
| Judgment and Decision Making | 3.6 | |
| Systems Evaluation | 3.6 | |
| Social Perceptiveness | 3.4 | |
| Operations Analysis | 3.3 | |
| Systems Analysis | 3.3 |
| Oral Comprehension | 4.0 | |
| Written Comprehension | 4.0 | |
| Oral Expression | 4.0 | |
| Written Expression | 4.0 | |
| Deductive Reasoning | 4.0 | |
| Inductive Reasoning | 4.0 | |
| Fluency of Ideas | 3.9 | |
| Problem Sensitivity | 3.9 | |
| Information Ordering | 3.9 | |
| Category Flexibility | 3.8 | |
| Originality | 3.6 | |
| Speech Clarity | 3.6 | |
| Mathematical Reasoning | 3.5 | |
| Near Vision | 3.5 | |
| Speech Recognition | 3.4 | |
| Number Facility | 3.3 | |
| Visualization | 3.3 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Showing the top 40 of 47.
Showing the top 40 of 62.
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
What to study: Engineering , Engineering/Engineering-Related Technologies/Technicians . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Share of people in this occupation at each level of education.
| Master's Degree | 50.0% | |
| Bachelor's Degree | 40.0% | |
| Post-Baccalaureate Certificate | 10.0% |
The interests and personal qualities O*NET associates with people who do this work.
| Investigative | 6.2 | |
| Realistic | 4.5 | |
| Conventional | 4.4 | |
| Artistic | 2.6 | |
| Enterprising | 2.6 |
| Engineering | 5.1 | |
| Mathematics/Statistics | 4.4 | |
| Social Science | 4.1 | |
| Information Technology | 3.4 | |
| Mechanics/Electronics | 3.2 | |
| Management/Administration | 2.4 | |
| Health Care Service | 2.2 |
| Dependability | 4.0 | |
| Attention to Detail | 3.0 | |
| Intellectual Curiosity | 2.4 | |
| Innovation | 2.4 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $70,000 |
| 25th percentile | $81,910 |
| Median (50th) | $101,140 |
| 75th percentile | $127,480 |
| 90th percentile | $157,140 |
| People employed | 350,230 |
Wages and employment are reported by BLS for the broader occupation group this specialty belongs to (SOC 17-2112), not for the specialty alone.
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Manufacturing · Sector | 237,030 | $100,060 |
| Professional, Scientific, and Technical Services · Sector | 50,290 | $106,420 |
| Engineering Services · National industry | 20,150 | $101,930 |
| Management of Companies and Enterprises · Sector | 15,770 | $115,210 |
| Wholesale Trade · Sector | 15,570 | $101,700 |
| Transportation and Warehousing · Sector | 7,860 | $97,440 |
| Testing Laboratories and Services · National industry | 4,630 | $102,360 |
| Machine Shops · National industry | 3,050 | $83,820 |
| Information · Sector | 2,170 | $128,220 |
| Mining, Quarrying, and Oil and Gas Extraction · Sector | 2,110 | $148,850 |
| Construction · Sector | 2,000 | $96,320 |
| Other Services (except Public Administration) · Sector | 1,380 | $92,320 |
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Testing Laboratories and Services · National industry | 11.96× | 4,630 |
| Manufacturing · Sector | 8.18× | 237,030 |
| Engineering Services · National industry | 7.67× | 20,150 |
| Machine Shops · National industry | 5.17× | 3,050 |
| Management of Companies and Enterprises · Sector | 2.47× | 15,770 |
| Professional, Scientific, and Technical Services · Sector | 2.06× | 50,290 |
| Mining, Quarrying, and Oil and Gas Extraction · Sector | 1.62× | 2,110 |
| Nuclear Electric Power Generation · National industry | 1.42× | 120 |
Part of the Advanced Manufacturing career cluster.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
Options the data surfaces for Human Factors Engineers and Ergonomists — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Capabilities this work builds that are used across many other occupations.
Occupations O*NET rates as related — the nearby moves on the map.
How people typically prepare for this work.
On the global GenAI exposure gradient this work sits around the 68th percentile of 427 international occupations.
Human Factors Engineers and Ergonomists show 81st-percentile AI task overlap — and about 25,200 annual U.S. openings
Human Factors Engineers and Ergonomists show 81st-percentile AI task overlap — and about 25,200 annual U.S. openings • Human Factors Engineers and Ergonomists rank in the 81st percentile (High band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 25,200 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be growing fast (+11%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $101,140, across about 350,230 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 57% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2) Source: Singulariki — "Human Factors Engineers and Ergonomists". https://singulariki.com/roles/role-17-2112-01 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
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Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
Data compiled June 2, 2026. Figures are estimates, not advice.
Singulariki. "Human Factors Engineers and Ergonomists." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-17-2112-01
Singulariki. (2026). Human Factors Engineers and Ergonomists. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-17-2112-01
@misc{singulariki-role-17-2112-01,
title = {Human Factors Engineers and Ergonomists},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-17-2112-01}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.