A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-17-2199-06/context.md directly.
AI work map
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.
Use as a copilot
Task areas where people work with AI — iterating, learning, or checking —
staying in the loop rather than handing the task off.
Investigate characteristics such as cost, performance, or process capability of potential microelectromechanical systems (MEMS) device designs, using simulation or modeling software. · 0.9%
Develop or validate specialized materials characterization procedures, such as thermal withstand, fatigue, notch sensitivity, abrasion, or hardness tests. · 0.9%
Conduct experimental or virtual studies to investigate characteristics and processing principles of potential microelectromechanical systems (MEMS) technology. · 0.8%
Task areas where a human was still judged necessary in a large share of observed
conversations — not a safety ruling, an observed-need signal.
Develop customer documentation, such as performance specifications, training manuals, or operating instructions. · 96.8% need a human
Conduct experimental or virtual studies to investigate characteristics and processing principles of potential microelectromechanical systems (MEMS) technology. · 86.6% need a human
Develop or validate specialized materials characterization procedures, such as thermal withstand, fatigue, notch sensitivity, abrasion, or hardness tests. · 78.4% need a human
↔67th-percentile task overlap — yet
about 9,300 openings a year
(+2.1% projected, BLS), and
observed AI use leans 5919% copilot, not hand-off (AEI)
. What exposure means →
AI & job outlook
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.
Exposure to current AI
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
67th
0.8
LLM task exposure, γ (OpenAI / Eloundou) Moderate
64th
0.8
AI assistant applicability (Microsoft) High
71st
0.2
OpenAI's exposure study scores tasks three ways: with a language model alone
(α 0.2), with simple added tooling
(β 0.5), and including AI-powered software
(γ 0.8). 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.
Historical automation estimate (2013)
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 ·
9th percentile among occupations ·
Low
How AI is actually used in this job
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 characteristics such as cost, performance, or process capability of potential microelectromechanical systems (MEMS) device designs, using simulation or modeling software.
4.4%
Conduct experimental or virtual studies to investigate characteristics and processing principles of potential microelectromechanical systems (MEMS) technology.
1.6%
Design or develop energy products using nanomaterials or nanoprocesses, such as micro-nano machining.
0.9%
Create schematics and physical layouts of integrated microelectromechanical systems (MEMS) components or packaged assemblies consistent with process, functional, or package constraints.
0.8%
Develop or validate specialized materials characterization procedures, such as thermal withstand, fatigue, notch sensitivity, abrasion, or hardness tests.
0.7%
Propose product designs involving microelectromechanical systems (MEMS) technology, considering market data or customer requirements.
0.5%
Job outlook
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 —
a labor-market forecast, not an AI-impact forecast.
Outlook
About average · +2.1% by 2034
Projected annual openings
9,300
Employment 2024 → 2034
158,800 → 162,100
“Annual openings” counts new jobs plus replacements for workers who leave the
occupation, so it can be large even when growth is modest.
Where this work sits on the global GenAI gradient
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.
Microsystems Engineers sits at the 57th percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure rose from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
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.
Working with AI in this job
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
59.2% working with AI · 6.5% 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)
40.8%
What people delegate to AI
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 characteristics such as cost, performance, or process capability of potential microelectromechanical systems (MEMS) device designs, using simulation or modeling software.
Learning
0.9%
Develop or validate specialized materials characterization procedures, such as thermal withstand, fatigue, notch sensitivity, abrasion, or hardness tests.
Learning
0.9%
Conduct experimental or virtual studies to investigate characteristics and processing principles of potential microelectromechanical systems (MEMS) technology.
Learning
0.8%
Develop customer documentation, such as performance specifications, training manuals, or operating instructions.
Iteration
0.3%
Where a human is still needed
Tasks where the model most often judged that a person remained necessary — a useful
read on the current boundary, not a guarantee.
Develop customer documentation, such as performance specifications, training manuals, or operating instructions.
96.8%
Conduct experimental or virtual studies to investigate characteristics and processing principles of potential microelectromechanical systems (MEMS) technology.
86.6%
Develop or validate specialized materials characterization procedures, such as thermal withstand, fatigue, notch sensitivity, abrasion, or hardness tests.
78.4%
Investigate characteristics such as cost, performance, or process capability of potential microelectromechanical systems (MEMS) device designs, using simulation or modeling software.
77.4%
What people most often hand AI here
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 characteristics such as cost, performance, or process capability of potential microelectromechanical systems (MEMS) device designs, using simulation or modeling software.
From: Investigate characteristics such as cost, performance, or process capability of potential microelectromechanical systems (MEMS) device designs, using simulation or modeling software. · 0.9% of measured AI use · learning
Help me develop or validate specialized materials characterization procedures, such as thermal withstand, fatigue, notch sensitivity, abrasion, or hardness tests.
From: Develop or validate specialized materials characterization procedures, such as thermal withstand, fatigue, notch sensitivity, abrasion, or hardness tests. · 0.9% of measured AI use · learning
Help me conduct experimental or virtual studies to investigate characteristics and processing principles of potential microelectromechanical systems (MEMS) technology.
From: Conduct experimental or virtual studies to investigate characteristics and processing principles of potential microelectromechanical systems (MEMS) technology. · 0.8% of measured AI use · learning
Help me develop customer documentation, such as performance specifications, training manuals, or operating instructions.
From: Develop customer documentation, such as performance specifications, training manuals, or operating instructions. · 0.3% of measured AI use · task iteration
Tasks
All 31 tasks O*NET lists for this occupation, ordered by importance.
Each links to its own page with AI-exposure and observed-use detail.
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.
Most of these occupations require graduate school. For example, they may require a master's degree, and some require a Ph.D., M.D., or J.D. (law degree).
Extensive skill, knowledge, and experience are needed for these occupations. Many require more than five years of experience. For example, surgeons must complete four years of college and an additional five to seven years of specialized medical training to be able to do their job.
Preparation level
SVP (8.0 and above) — total schooling plus on-the-job experience.
Annual wages by percentile — U.S. (BLS OEWS). The light
band spans the 10th–90th percentile; the darker band is the middle half (25th–75th); the line
is the median.
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for
the occupation, not an AI-impact forecast.
10th percentile
$62,840
25th percentile
$85,750
Median (50th)
$117,750
75th percentile
$152,670
90th percentile
$183,510
People employed
150,750
Wages and employment are reported by BLS for the broader occupation group this
specialty belongs to (SOC 17-2199), not for the specialty alone.
Industries that employ this occupation
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is
the occupation's national median, not industry-specific.
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).
AI task-overlap percentile (horizontal) vs. median-pay percentile (vertical), across all scored occupations. This occupation is highlighted; related occupations are plotted alongside it. Overlap measures shared tasks with AI, not automation.
▸Write a report on thisheadline · factoids · citation
Microsystems Engineers show 67th-percentile AI task overlap — and about 9,300 annual U.S. openings
Microsystems Engineers rank in the 67th 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 9,300 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 about average (+2.1%) from 2024 to 2034.BLS Employment Projections 2024–34
Median annual pay is $117,750, across about 150,750 U.S. workers.BLS OEWS (May 2024)
Of the AI use actually observed for this work, 59% 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
Copy the whole kit
Microsystems Engineers show 67th-percentile AI task overlap — and about 9,300 annual U.S. openings
• Microsystems Engineers rank in the 67th 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 9,300 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 about average (+2.1%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $117,750, across about 150,750 U.S. workers. (BLS OEWS (May 2024))
• Of the AI use actually observed for this work, 59% 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 — "Microsystems Engineers". https://singulariki.com/roles/role-17-2199-06
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
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.
Sources for this page
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.
O*NET 30.3U.S. Department of Labor / National Center for O*NET Development
Data compiled June 2, 2026. Figures are estimates, not advice.
Cite this page
Plain
Singulariki. "Microsystems 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-17-2199-06
APA
Singulariki. (2026). Microsystems Engineers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-17-2199-06
BibTeX
@misc{singulariki-role-17-2199-06,
title = {Microsystems 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-17-2199-06}
}
Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.
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