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Model Makers, Metal and Plastic

Occupation · SOC 51-4061.00

Set up and operate machines, such as lathes, milling and engraving machines, and jig borers to make working models of metal or plastic objects. Includes template makers.

Also called: CNC Machinist (Computer Numerical Control Machinist) · Model Builder · Model Maker · Molding Technician (Molding Tech) · CNC Programmer (Computer Numerical Control Programmer) · Metal Model Maker · Model Maker Machinist · Model Technician (Model Tech) · Pattern Finisher · Prototype Special Build · Aircraft Mockup Builder · Appliances Sample Maker

Job family: Production Occupations

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Download .md

A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-51-4061-00/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.

21st-percentile task overlap — yet about 300 openings a year (-18.2% projected, BLS) . 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.) Low 27th -0.7
LLM task exposure, γ (OpenAI / Eloundou) Low 24th 0.2
AI assistant applicability (Microsoft) Low 17th 0.1

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.2), and including AI-powered software (γ 0.2). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.

This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.

Mixed signals. Today's AI/LLM studies show relatively low exposure for this job, but the older (2013) Frey–Osborne work rated it higher for computerization and robotics. Different eras, different technologies — the AI measures above reflect the current state.

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 0.9 · 84th percentile among occupations · High

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

Outlook Declining · -18.2% by 2034
Projected annual openings 300
Employment 2024 → 2034 3,200 → 2,600

“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.

20% mean task exposure (2025)
34th percentile of 427 placed occupations
−1 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Toolmakers and Related Workers · 7222 20% Not exposed

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.

Tasks

All 16 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

Work activities

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Knowledge

Mechanical 4.3
Mathematics 4.0
Production and Processing 3.9
Design 3.9
Engineering and Technology 3.8
Computers and Electronics 2.9

Abilities

Near Vision 4.0
Visualization 3.8
Arm-Hand Steadiness 3.6
Manual Dexterity 3.6
Finger Dexterity 3.6
Control Precision 3.6
Written Comprehension 3.5
Information Ordering 3.5
Oral Comprehension 3.4
Problem Sensitivity 3.4
Deductive Reasoning 3.4
Reaction Time 3.4
Selective Attention 3.3
Multilimb Coordination 3.3
Oral Expression 3.0
Written Expression 3.0
Inductive Reasoning 3.0
Category Flexibility 3.0
Perceptual Speed 3.0
Rate Control 3.0
Static Strength 3.0
Trunk Strength 3.0
Far Vision 3.0
Auditory Attention 3.0

Transferable skills

Operation and Control 3.6
Operations Monitoring 3.5
Quality Control Analysis 3.3
Equipment Selection 3.0
Troubleshooting 3.0
Judgment and Decision Making 3.0
Time Management 3.0

Essential skills

Critical Thinking 3.3
Monitoring 3.1
Reading Comprehension 2.9

Skills in demand

Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.

Tools & technology

Example Category
Microsoft Excel Spreadsheet software Hot technology
Microsoft Office software Office suite software Hot technology
Microsoft Word Word processing software Hot technology
CNC Software Mastercam Computer aided manufacturing CAM software
PTC Creo Parametric Computer aided design CAD software

Work context

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.

Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets 5.0
Importance of Being Exact or Accurate 4.8
Face-to-Face Discussions with Individuals and Within Teams 4.6
Spend Time Standing 4.6
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 4.5
Time Pressure 4.2
Indoors, Environmentally Controlled 4.0
Freedom to Make Decisions 3.9
Importance of Repeating Same Tasks 3.7
Determine Tasks, Priorities and Goals 3.5
Work With or Contribute to a Work Group or Team 3.5
Pace Determined by Speed of Equipment 3.5
Contact With Others 3.3
Spend Time Making Repetitive Motions 3.3
Exposed to Minor Burns, Cuts, Bites, or Stings 3.3
Exposed to Contaminants 3.2
Frequency of Decision Making 3.2
Impact of Decisions on Co-workers or Company Results 3.1
Exposed to Hazardous Equipment 3.0
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 2.9
Coordinate or Lead Others in Accomplishing Work Activities 2.9
Spend Time Walking or Running 2.9
Physical Proximity 2.8
Health and Safety of Other Workers 2.7
In an Open Vehicle or Operating Equipment 2.5
Consequence of Error 2.4
Level of Competition 2.4
Degree of Automation 2.3
Dealing With Unpleasant, Angry, or Discourteous People 2.3
Indoors, Not Environmentally Controlled 2.3
Work Outcomes and Results of Other Workers 2.2
Spend Time Keeping or Regaining Balance 2.0
Spend Time Bending or Twisting Your Body 1.9
Conflict Situations 1.9
E-Mail 1.9
Exposed to Very Hot or Cold Temperatures 1.8
Telephone Conversations 1.6
Spend Time Kneeling, Crouching, Stooping, or Crawling 1.6
Written Letters and Memos 1.5
Exposed to Hazardous Conditions 1.5

How to get in

Job zone
Zone 3 — Job Zone Three: Medium Preparation Needed
Education
Most occupations in this zone require training in vocational schools, related on-the-job experience, or an associate's degree.
Typical entry-level education
High school diploma or equivalent · BLS, the typical path — not a requirement
Related experience
Previous work-related skill, knowledge, or experience is required for these occupations. For example, an electrician must have completed three or four years of apprenticeship or several years of vocational training, and often must have passed a licensing exam, in order to perform the job.
Preparation level
SVP (6.0 to < 7.0) — total schooling plus on-the-job experience.

What to study: Precision Production . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.

Education of current workers

Share of people in this occupation at each level of education.

Post-Secondary Certificate 37.7%
Some College Courses 15.5%
Associate's Degree (or other 2-year degree) 5.0%
Post-Master's Certificate 0.8%

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Career interests (Holland / RIASEC)

Realistic 6.9
Conventional 4.0
Investigative 2.9
Artistic 2.7

Interest areas

Mechanics/Electronics 5.6
Engineering 5.2
Physical/Manual Labor 4.6
Construction/Woodwork 2.3
Information Technology 2.1
Transportation/Machine Operation 2.1
Applied Arts and Design 2.0
Mathematics/Statistics 2.0

Work styles

Attention to Detail 2.7
Dependability 2.2
Cautiousness 2.0
Achievement Orientation 1.6

Wages & employment

U.S. · annual wages (BLS OEWS)

$38k10th$49k25th$63kMedian$79k75th$96k90th
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.
3k20243k2034 (proj.)-18.2% · Declining
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $38,430
25th percentile $48,860
Median (50th) $62,700
75th percentile $79,470
90th percentile $95,780
People employed 3,230

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.

Industry Workers National median pay
Manufacturing · Sector 2,110 $54,840
Professional, Scientific, and Technical Services · Sector 840 $76,060
Engineering Services · National industry 350 $94,750
Wholesale Trade · Sector 110 $82,050
Management of Companies and Enterprises · Sector 70 $74,530
Jewelry and Silverware Manufacturing · National industry 50 $33,330
Machine Shops · National industry $60,650

Where this work is most concentrated

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 14.45× 350
Manufacturing · Sector 7.89× 2,110
Professional, Scientific, and Technical Services · Sector 3.72× 840
Wholesale Trade · Sector 0.87× 110

Part of the Advanced Manufacturing career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Model Makers, Metal and Plastic sits at the 21st percentile of AI task-overlap and the 51st percentile of median pay, placed here against 10 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Model Makers, Metal and Plastic Structural Metal Fabricators and Fitters Molders, Shapers, and Casters, Except Metal and Plastic Engine and Other Machine Assemblers Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic AI task-overlap percentile → ↑ Median pay
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.

Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.

What you can do with this

Options the data surfaces for Model Makers, Metal and Plastic — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Skills that travel

Capabilities this work builds that are used across many other occupations.

Paths in

How people typically prepare for this work.

Zoom out

On the global GenAI exposure gradient this work sits around the 34th percentile of 427 international occupations.

Write a report on thisheadline · factoids · citation

Model Makers, Metal and Plastic show 21st-percentile AI task overlap — and about 300 annual U.S. openings

  • Model Makers, Metal and Plastic rank in the 21st percentile (Low 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 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 declining (-18.2%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $62,700, across about 3,230 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Model Makers, Metal and Plastic show 21st-percentile AI task overlap — and about 300 annual U.S. openings

• Model Makers, Metal and Plastic rank in the 21st percentile (Low 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 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 declining (-18.2%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $62,700, across about 3,230 U.S. workers. (BLS OEWS (May 2024))

Source: Singulariki — "Model Makers, Metal and Plastic". https://singulariki.com/roles/role-51-4061-00
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.

AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom

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.

Data compiled June 2, 2026. Figures are estimates, not advice.

Cite this page
Plain

Singulariki. "Model Makers, Metal and Plastic." 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; 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-51-4061-00

APA

Singulariki. (2026). Model Makers, Metal and Plastic. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-51-4061-00

BibTeX
@misc{singulariki-role-51-4061-00,
  title  = {Model Makers, Metal and Plastic},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; 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-51-4061-00}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.

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