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Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic

Occupation · SOC 51-4021.00

Set up, operate, or tend machines to extrude or draw thermoplastic or metal materials into tubes, rods, hoses, wire, bars, or structural shapes.

Also called: Equipment Technician · Extruder Operator · Extrusion Press Operator · Machine Operator · Extrusion Operator · Metal Inspector · Setup Operator · Wire Mill Operator · Wire Mill Rover · Core Extruder · Core Shaper · Draw Machine Operator

Job family: Production Occupations

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A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-51-4021-00/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.

18th-percentile task overlap — yet about 6,500 openings a year (+1.2% 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 9th -1.2
LLM task exposure, γ (OpenAI / Eloundou) Low 7th 0.0
AI assistant applicability (Microsoft) Moderate 48th 0.1

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.0), with simple added tooling (β 0.0), and including AI-powered software (γ 0.0). 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 · 81st percentile among occupations · High

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.

Troubleshoot, maintain, and make minor repairs to equipment. 0.2%

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 · +1.2% by 2034
Projected annual openings 6,500
Employment 2024 → 2034 66,000 → 66,800

“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 3 occupations below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.

21% mean task exposure (2025)
37th percentile of 427 placed occupations
+1 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Metal Processing Plant Operators · 8121 27% Not exposed
Metal Working Machine Tool Setters and Operators · 7223 18% Not exposed
Plastic Products Machine Operators · 8142 18% 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

Production and Processing 4.0
Mathematics 3.5
English Language 3.5

Abilities

Control Precision 3.8
Manual Dexterity 3.6
Multilimb Coordination 3.6
Reaction Time 3.6
Problem Sensitivity 3.4
Rate Control 3.4
Near Vision 3.4
Trunk Strength 3.3
Information Ordering 3.1
Flexibility of Closure 3.1
Arm-Hand Steadiness 3.1
Static Strength 3.1
Oral Comprehension 3.0
Deductive Reasoning 3.0
Perceptual Speed 3.0
Visualization 3.0
Selective Attention 3.0
Finger Dexterity 3.0
Stamina 3.0
Far Vision 3.0
Visual Color Discrimination 3.0
Auditory Attention 3.0
Speech Recognition 3.0
Speech Clarity 3.0

Transferable skills

Operations Monitoring 3.5
Operation and Control 3.4
Quality Control Analysis 3.1
Judgment and Decision Making 3.1
Coordination 3.0
Complex Problem Solving 3.0
Troubleshooting 3.0
Time Management 2.9

Essential skills

Critical Thinking 3.1
Monitoring 3.1
Speaking 3.0
Reading Comprehension 2.9
Active Listening 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 Outlook Electronic mail software Hot technology
Microsoft PowerPoint Presentation software Hot technology
Microsoft Word Word processing software Hot technology
SAP software Enterprise resource planning ERP software Hot technology
Enterprise application integration EAI software Enterprise application integration software
Operational databases Data base user interface and query 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
Pace Determined by Speed of Equipment 4.8
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 4.7
Frequency of Decision Making 4.6
Spend Time Standing 4.4
Time Pressure 4.4
Impact of Decisions on Co-workers or Company Results 4.4
Exposed to Contaminants 4.3
Consequence of Error 4.2
Spend Time Walking or Running 4.0
Importance of Being Exact or Accurate 4.0
Face-to-Face Discussions with Individuals and Within Teams 4.0
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 3.9
Exposed to Minor Burns, Cuts, Bites, or Stings 3.8
Exposed to Hazardous Equipment 3.8
Freedom to Make Decisions 3.7
Indoors, Not Environmentally Controlled 3.6
Dealing With Unpleasant, Angry, or Discourteous People 3.5
Work With or Contribute to a Work Group or Team 3.5
Spend Time Bending or Twisting Your Body 3.5
Importance of Repeating Same Tasks 3.5
Contact With Others 3.5
Spend Time Making Repetitive Motions 3.4
Exposed to Very Hot or Cold Temperatures 3.3
Physical Proximity 3.0
Determine Tasks, Priorities and Goals 3.0
Health and Safety of Other Workers 2.9
Exposed to Hazardous Conditions 2.8
Work Outcomes and Results of Other Workers 2.8
Degree of Automation 2.7
Telephone Conversations 2.5
Coordinate or Lead Others in Accomplishing Work Activities 2.5
Conflict Situations 2.5
Indoors, Environmentally Controlled 2.5
Exposed to Extremely Bright or Inadequate Lighting Conditions 2.4
Level of Competition 2.4
Exposed to Cramped Work Space, Awkward Positions 2.3
Wear Specialized Protective or Safety Equipment such as Breathing Apparatus, Safety Harness, Full Protection Suits, or Radiation Protection 2.2
Spend Time Keeping or Regaining Balance 2.0
In an Open Vehicle or Operating Equipment 2.0

How to get in

Job zone
Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
Education
Usually requires a high school diploma or GED, though some occupations may not.
Typical entry-level education
High school diploma or equivalent · BLS, the typical path — not a requirement
Related experience
Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
Preparation level
SVP (Below 6.0) — 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.

High School Diploma 84.7%
Post-Secondary Certificate 11.2%
Less than a High School Diploma 3.6%
Doctoral Degree 0.5%

Interests & work styles

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

Career interests (Holland / RIASEC)

Realistic 6.7
Conventional 4.3
Investigative 2.2
Artistic 1.4

Interest areas

Physical/Manual Labor 3.8
Mechanics/Electronics 3.5
Engineering 2.6
Transportation/Machine Operation 2.0
Mathematics/Statistics 1.6
Construction/Woodwork 1.5
Physical Science 1.4
Accounting 1.2
Information Technology 1.2

Work styles

Attention to Detail 2.4
Dependability 2.1
Cautiousness 2.0

Wages & employment

U.S. · annual wages (BLS OEWS)

$35k10th$39k25th$47kMedian$54k75th$62k90th
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.
66k202467k2034 (proj.)+1.2% · About average
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $35,390
25th percentile $39,370
Median (50th) $46,980
75th percentile $54,360
90th percentile $62,470
People employed 65,700

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 63,050 $47,040
Wholesale Trade · Sector 1,340 $48,380
Administrative and Support and Waste Management and Remediation Services · Sector 990 $34,550
Temporary Help Services · National industry 980 $34,150
Machine Shops · National industry 220 $48,800
Construction · Sector 80 $44,340
Professional, Scientific, and Technical Services · Sector $51,970

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
Manufacturing · Sector 11.59× 63,050
Machine Shops · National industry 1.99× 220
Temporary Help Services · National industry 0.87× 980
Wholesale Trade · Sector 0.52× 1,340
Administrative and Support and Waste Management and Remediation Services · Sector 0.26× 990

Part of the Advanced Manufacturing career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic sits at the 18th percentile of AI task-overlap and the 25th percentile of median pay, placed here against 12 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers Molding, Coremaking, and Casting Machine Setters, Operators, and Tenders, Metal and Plastic Paper Goods Machine Setters, Operators, and Tenders 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 Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Write a report on thisheadline · factoids · citation

Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic show 18th-percentile AI task overlap — and about 6,500 annual U.S. openings

  • Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic rank in the 18th 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 6,500 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 (+1.2%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $46,980, across about 65,700 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic show 18th-percentile AI task overlap — and about 6,500 annual U.S. openings

• Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic rank in the 18th 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 6,500 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 (+1.2%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $46,980, across about 65,700 U.S. workers. (BLS OEWS (May 2024))

Source: Singulariki — "Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic". https://singulariki.com/roles/role-51-4021-00
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. "Extruding and Drawing Machine Setters, Operators, and Tenders, 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; 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-51-4021-00

APA

Singulariki. (2026). Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-51-4021-00

BibTeX
@misc{singulariki-role-51-4021-00,
  title  = {Extruding and Drawing Machine Setters, Operators, and Tenders, 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; 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-51-4021-00}
}

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

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