Occupation · SOC 17-2061.00
Research, design, develop, or test computer or computer-related equipment for commercial, industrial, military, or scientific use. May supervise the manufacturing and installation of computer or computer-related equipment and components.
Also called: Design Engineer · Engineer · Hardware Engineer · Systems Integration Engineer · Field Service Engineer · Hardware Design Engineer · Physical Design Engineer · Project Engineer · Staff Engineer · Analog Design Engineer · Analog IC Design Engineer (Analog Integrated Circuit Design Engineer) · Application Specific Integrated Circuit Design Engineer (ASIC Design Engineer)
Job family: Architecture and Engineering Occupations
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-17-2061-00/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 most often handled directively in observed AI conversations — candidates to delegate with light review.
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.
88th-percentile task overlap — yet about 4,700 openings a year (+7.3% projected, BLS), and observed AI use leans 5219% 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 | 79th | 0.9 | |
| AI assistant applicability (Microsoft) High | 91st | 0.3 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.5), with simple added tooling (β 0.7), 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 0.2 · 34th percentile among occupations · Moderate
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.
| Analyze information to determine, recommend, and plan layout, including type of computers and peripheral equipment modifications. | 59.9% | |
| Store, retrieve, and manipulate data for analysis of system capabilities and requirements. | 10.1% | |
| Confer with engineering staff and consult specifications to evaluate interface between hardware and software and operational and performance requirements of overall system. | 1.1% | |
| Write detailed functional specifications that document the hardware development process and support hardware introduction. | 1.0% | |
| Analyze user needs and recommend appropriate hardware. | 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 · +7.3% by 2034 |
| Projected annual openings | 4,700 |
| Employment 2024 → 2034 | 76,800 → 82,400 |
“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 |
|---|---|---|
| Electronics Engineers · 2152 | 42% | Gradient 2 |
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 | 52.2% working with AI · 42.9% handed to AI |
| Most common way people use AI here | Directive · AI does it; you give the instruction |
| Typical AI autonomy | 4.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 56.7% |
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 |
|---|---|---|
| Store, retrieve, and manipulate data for analysis of system capabilities and requirements. | Directive | 21.5% |
| Analyze information to determine, recommend, and plan layout, including type of computers and peripheral equipment modifications. | Learning | 19.5% |
| Write detailed functional specifications that document the hardware development process and support hardware introduction. | Iteration | 1.2% |
| Evaluate factors such as reporting formats required, cost constraints, and need for security restrictions to determine hardware configuration. | Learning | 0.9% |
| Analyze user needs and recommend appropriate hardware. | Directive | 0.8% |
| Confer with engineering staff and consult specifications to evaluate interface between hardware and software and operational and performance requirements of overall system. | Learning | 0.8% |
| Design and develop computer hardware and support peripherals, including central processing units (CPUs), support logic, microprocessors, custom integrated circuits, and printers and disk drives. | — | 0.3% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Analyze user needs and recommend appropriate hardware. | 100.0% | |
| Evaluate factors such as reporting formats required, cost constraints, and need for security restrictions to determine hardware configuration. | 97.7% | |
| Analyze information to determine, recommend, and plan layout, including type of computers and peripheral equipment modifications. | 92.3% | |
| Write detailed functional specifications that document the hardware development process and support hardware introduction. | 90.0% | |
| Design and develop computer hardware and support peripherals, including central processing units (CPUs), support logic, microprocessors, custom integrated circuits, and printers and disk drives. | 90.0% | |
| Confer with engineering staff and consult specifications to evaluate interface between hardware and software and operational and performance requirements of overall system. | 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 store, retrieve, and manipulate data for analysis of system capabilities and requirements. From: Store, retrieve, and manipulate data for analysis of system capabilities and requirements. · 21.5% of measured AI use · directive
Help me analyze information to determine, recommend, and plan layout, including type of computers and peripheral equipment modifications. From: Analyze information to determine, recommend, and plan layout, including type of computers and peripheral equipment modifications. · 19.5% of measured AI use · learning
Help me write detailed functional specifications that document the hardware development process and support hardware introduction. From: Write detailed functional specifications that document the hardware development process and support hardware introduction. · 1.2% of measured AI use · task iteration
Help me evaluate factors such as reporting formats required, cost constraints, and need for security restrictions to determine hardware configuration. From: Evaluate factors such as reporting formats required, cost constraints, and need for security restrictions to determine hardware configuration. · 0.9% of measured AI use · learning
All 18 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| Computers and Electronics | 4.7 | |
| Engineering and Technology | 4.7 | |
| Design | 4.1 | |
| Mathematics | 4.1 | |
| English Language | 3.8 | |
| Physics | 3.4 | |
| Telecommunications | 3.1 |
| Reading Comprehension | 4.0 | |
| Critical Thinking | 4.0 | |
| Active Listening | 3.9 | |
| Writing | 3.9 | |
| Speaking | 3.9 | |
| Active Learning | 3.8 | |
| Mathematics | 3.3 | |
| Monitoring | 3.1 |
| Oral Comprehension | 4.0 | |
| Written Comprehension | 4.0 | |
| Oral Expression | 4.0 | |
| Deductive Reasoning | 4.0 | |
| Inductive Reasoning | 4.0 | |
| Information Ordering | 4.0 | |
| Problem Sensitivity | 3.9 | |
| Written Expression | 3.8 | |
| Near Vision | 3.6 | |
| Originality | 3.5 | |
| Category Flexibility | 3.5 | |
| Speech Clarity | 3.4 | |
| Fluency of Ideas | 3.3 | |
| Visualization | 3.3 | |
| Speech Recognition | 3.3 | |
| Mathematical Reasoning | 3.1 | |
| Number Facility | 3.1 | |
| Flexibility of Closure | 3.1 |
| Complex Problem Solving | 3.9 | |
| Judgment and Decision Making | 3.6 | |
| Operations Analysis | 3.4 | |
| Systems Analysis | 3.4 | |
| Systems Evaluation | 3.4 | |
| Coordination | 3.1 | |
| Time Management | 3.1 |
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 43.
Showing the top 40 of 112.
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 . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Share of people in this occupation at each level of education.
| Bachelor's Degree | 48.3% | |
| Master's Degree | 34.5% | |
| Some College Courses | 6.9% | |
| Doctoral Degree | 6.9% | |
| First Professional Degree | 3.5% |
The interests and personal qualities O*NET associates with people who do this work.
| Engineering | 6.6 | |
| Information Technology | 6.3 | |
| Mechanics/Electronics | 5.4 | |
| Mathematics/Statistics | 3.8 | |
| Physical Science | 3.3 | |
| Management/Administration | 2.1 |
| Dependability | 6.0 | |
| Attention to Detail | 5.0 | |
| Intellectual Curiosity | 4.0 | |
| Achievement Orientation | 3.0 | |
| Innovation | 2.5 | |
| Adaptability | 2.0 |
| Realistic | 5.7 | |
| Investigative | 5.6 | |
| Conventional | 5.4 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $85,430 |
| 25th percentile | $115,500 |
| Median (50th) | $155,020 |
| 75th percentile | $185,380 |
| 90th percentile | $223,820 |
| People employed | 75,710 |
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 |
|---|---|---|
| Professional, Scientific, and Technical Services · Sector | 31,660 | $167,130 |
| Manufacturing · Sector | 25,600 | $160,210 |
| Information · Sector | 6,260 | $160,080 |
| Engineering Services · National industry | 4,690 | $135,000 |
| Wholesale Trade · Sector | 3,830 | $135,680 |
| Management of Companies and Enterprises · Sector | 1,690 | $140,170 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 930 | $137,140 |
| Temporary Help Services · National industry | 740 | $132,880 |
| Finance and Insurance · Sector | 300 | $150,860 |
| Educational Services · Sector | 180 | $86,700 |
| Transportation and Warehousing · Sector | 130 | $56,050 |
| Utilities · Sector | 80 | $116,870 |
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 |
|---|---|---|
| Engineering Services · National industry | 8.26× | 4,690 |
| Professional, Scientific, and Technical Services · Sector | 5.99× | 31,660 |
| Information · Sector | 4.38× | 6,260 |
| Manufacturing · Sector | 4.08× | 25,600 |
| Wholesale Trade · Sector | 1.29× | 3,830 |
| Management of Companies and Enterprises · Sector | 1.23× | 1,690 |
| Temporary Help Services · National industry | 0.57× | 740 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 0.21× | 930 |
Part of the Digital Technology 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 Computer Hardware Engineers — 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 79th percentile of 427 international occupations.
Computer Hardware Engineers show 88th-percentile AI task overlap — and about 4,700 annual U.S. openings
Computer Hardware Engineers show 88th-percentile AI task overlap — and about 4,700 annual U.S. openings • Computer Hardware Engineers rank in the 88th 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 4,700 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 (+7.3%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $155,020, across about 75,710 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 52% 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 — "Computer Hardware Engineers". https://singulariki.com/roles/role-17-2061-00 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. "Computer Hardware Engineers." 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-17-2061-00
Singulariki. (2026). Computer Hardware Engineers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-17-2061-00
@misc{singulariki-role-17-2061-00,
title = {Computer Hardware Engineers},
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-17-2061-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.