Skills it runs on
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers
Occupation · SOC 51-6091.00
Set up, operate, or tend machines that extrude and form continuous filaments from synthetic materials, such as liquid polymer, rayon, and fiberglass.
Also called: Extruder Operator · Extrusion Line Operator · Extrusion Operator · Spindraw Operator · Extruder · Granulator · Hot End Operator · Pelletizer Operator · Pot Tipper · Stretch Operator · Beamer Operator · Beaming Machine Operator
Job family: Production Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-51-6091-00/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.
Market signal
Independent published positions, read together — not a forecast.
- Declining employment outlook (-1.1% by 2034)
- 2,000 openings/yr
- Low AI exposure
- Median pay $44,980/yr
- Work is mostly on-site (not teleworkable)
- 2013 flagged this for computerization (75th); today's AI task-overlap is comparatively low (10th)
10th-percentile task overlap — yet about 2,000 openings a year (-1.1% 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 | 23rd | -0.9 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 10th | 0.1 | |
| AI assistant applicability (Microsoft) Low | 8th | 0.0 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.1), and including AI-powered software (γ 0.1). 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 · 75th 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 · -1.1% by 2034 |
| Projected annual openings | 2,000 |
| Employment 2024 → 2034 | 15,200 → 15,000 |
“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.
Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers sits at the 23rd 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.
17% mean task exposure (2025)
23rd percentile of 427 placed occupations
+3 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Glass and Ceramics Plant Operators · 8181 | 17% | 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 21 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Set up, operate, or tend machines that extrude and form filaments from synthetic materials such as rayon, fiberglass, or liquid polymers. Core · importance 4.3
- Press buttons to stop machines when processes are complete or when malfunctions are detected. Core · importance 4.3
- Notify other workers of defects, and direct them to adjust extruding and forming machines. Core · importance 4.3
- Observe flow of finish across finish rollers, and turn valves to adjust flow to specifications. Supplemental · importance 4.3
- Observe machine operations, control boards, and gauges to detect malfunctions such as clogged bushings and defective binder applicators. Core · importance 4.2
- Remove polymer deposits from spinnerettes and equipment, using silicone spray, brass chisels, and bronze-wool pads. Supplemental · importance 4.0
- Load materials into extruding and forming machines, using hand tools, and adjust feed mechanisms to set feed rates. Core · importance 4.0
- Press metering-pump buttons and turn valves to stop flow of polymers. Supplemental · importance 3.9
- Record operational data on tags, and attach tags to machines. Supplemental · importance 3.9
- Move controls to activate and adjust extruding and forming machines. Core · importance 3.9
- Start metering pumps and observe operation of machines and equipment to ensure continuous flow of filaments extruded through spinnerettes and to detect processing defects. Supplemental · importance 3.9
- Remove excess, entangled, or completed filaments from machines, using hand tools. Supplemental · importance 3.9
- Record details of machine malfunctions. Core · importance 3.8
- Wipe finish rollers with cloths and wash finish trays with water when necessary. Supplemental · importance 3.8
- Clean and maintain extruding and forming machines, using hand tools. Core · importance 3.7
- Lower pans inside cabinets to catch molten filaments until flow of polymer through packs has stopped. Supplemental · importance 3.6
- Open cabinet doors to cut multifilament threadlines away from guides, using scissors. Supplemental · importance 3.3
- Pass sliver strands through openings in floors to workers on floors below who wind slivers onto tubes. Supplemental
- Pull extruded fiberglass filaments over sleeves where binding solution is applied, and into grooves of graphite shoes that bind filaments into single strands of sliver. Supplemental
- Turn petcocks to adjust the flow of binding fluid to sleeves. Supplemental
- Turn rheostats to obtain specified temperatures in electric furnaces where glass is melted. Supplemental
Work activities
- Clean production equipment.
- Operate metal or plastic forming equipment.
- Load materials into production equipment.
- Adjust equipment controls to regulate flow of production materials or products.
- Watch operating equipment to detect malfunctions.
- Operate pumping systems or equipment.
- Record operational or production data.
- Notify others of equipment repair or maintenance needs.
- Signal others to coordinate work activities.
- Monitor equipment operation to ensure proper functioning.
- Cut industrial materials in preparation for fabrication or processing.
- Maintain production or processing equipment.
- Mark products, workpieces, or equipment with identifying information.
- Position containers to receive materials or workpieces.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Transferable skills
| Operations Monitoring | 4.0 | |
| Operation and Control | 3.8 | |
| Coordination | 3.0 | |
| Complex Problem Solving | 3.0 | |
| Quality Control Analysis | 3.0 | |
| Judgment and Decision Making | 3.0 | |
| Time Management | 3.0 |
Abilities
| Problem Sensitivity | 3.8 | |
| Control Precision | 3.8 | |
| Near Vision | 3.5 | |
| Oral Comprehension | 3.4 | |
| Arm-Hand Steadiness | 3.4 | |
| Finger Dexterity | 3.4 | |
| Manual Dexterity | 3.3 | |
| Multilimb Coordination | 3.3 | |
| Reaction Time | 3.3 | |
| Oral Expression | 3.1 | |
| Deductive Reasoning | 3.1 | |
| Inductive Reasoning | 3.1 | |
| Information Ordering | 3.1 | |
| Rate Control | 3.1 | |
| Written Comprehension | 3.0 | |
| Category Flexibility | 3.0 | |
| Flexibility of Closure | 3.0 | |
| Perceptual Speed | 3.0 | |
| Visualization | 3.0 | |
| Selective Attention | 3.0 | |
| Time Sharing | 3.0 | |
| Trunk Strength | 3.0 | |
| Auditory Attention | 3.0 | |
| Speech Clarity | 3.0 |
Knowledge
| Production and Processing | 3.7 | |
| English Language | 3.2 | |
| Mathematics | 3.1 | |
| Education and Training | 3.1 | |
| Mechanical | 3.1 |
Essential skills
| Monitoring | 3.5 | |
| Active Listening | 3.0 | |
| Speaking | 3.0 | |
| 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 Outlook | Electronic mail software | Hot technology |
| Microsoft Word | Word processing software | Hot technology |
| SAP software | Enterprise resource planning ERP software | Hot technology |
| Apache Hadoop YARN | Data base management system software | |
| Camstar Manufacturing Execution System MES | Industrial control software | |
| Operational databases | Data base user interface and query software | |
| Statistical process control SPC software | Industrial control 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.
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.
Education of current workers
Share of people in this occupation at each level of education.
| High School Diploma | 74.1% | |
| Less than a High School Diploma | 19.4% | |
| Post-Secondary Certificate | 6.5% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Career interests (Holland / RIASEC)
| Realistic | 6.5 | |
| Conventional | 4.0 | |
| Investigative | 2.2 | |
| Enterprising | 1.4 | |
| Artistic | 1.3 | |
| Social | 1.2 |
Interest areas
| Mechanics/Electronics | 3.7 | |
| Physical/Manual Labor | 3.4 | |
| Engineering | 2.8 | |
| Transportation/Machine Operation | 2.2 | |
| Mathematics/Statistics | 1.6 | |
| Construction/Woodwork | 1.6 | |
| Physical Science | 1.5 |
Work styles
| Dependability | 2.1 | |
| Attention to Detail | 2.0 | |
| Cautiousness | 2.0 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $35,590 |
| 25th percentile | $38,990 |
| Median (50th) | $44,980 |
| 75th percentile | $54,690 |
| 90th percentile | $63,650 |
| People employed | 14,900 |
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 | 14,610 | $45,160 |
| Wholesale Trade · Sector | — | $39,200 |
| Administrative and Support and Waste Management and Remediation Services · Sector | — | $36,450 |
| Temporary Help Services · National industry | — | $36,450 |
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 | 11.85× | 14,610 |
Related occupations
Part of the Advanced Manufacturing career cluster.
- Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic · 51-4021.00 Compare →
- Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders · 51-9041.00 Compare →
- Paper Goods Machine Setters, Operators, and Tenders · 51-9196.00 Compare →
- Molding, Coremaking, and Casting Machine Setters, Operators, and Tenders, Metal and Plastic · 51-4072.00 Compare →
- Adhesive Bonding Machine Operators and Tenders · 51-9191.00 Compare →
- Rolling Machine Setters, Operators, and Tenders, Metal and Plastic · 51-4023.00 Compare →
- Cutting and Slicing Machine Setters, Operators, and Tenders · 51-9032.00 Compare →
- Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic · 51-4081.00 Compare →
- Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic · 51-4034.00 Compare →
- Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic · 51-4035.00 Compare →
- Machine Feeders and Offbearers · 53-7063.00 Compare →
- Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic · 51-4033.00 Compare →
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 Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers — 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.
Adjacent roles
Occupations O*NET rates as related — the nearby moves on the map.
- Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic Compare →
- Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders Compare →
- Paper Goods Machine Setters, Operators, and Tenders Compare →
- Molding, Coremaking, and Casting Machine Setters, Operators, and Tenders, Metal and Plastic Compare →
- Adhesive Bonding Machine Operators and Tenders Compare →
Paths in
How people typically prepare for this work.
- High school diploma or equivalent · typical entry level
- Job zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed · preparation tier
Zoom out
On the global GenAI exposure gradient this work sits around the 23rd percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers show 10th-percentile AI task overlap — and about 2,000 annual U.S. openings
- Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers rank in the 10th 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 2,000 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 (-1.1%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $44,980, across about 14,900 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers show 10th-percentile AI task overlap — and about 2,000 annual U.S. openings • Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers rank in the 10th 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 2,000 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 (-1.1%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $44,980, across about 14,900 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers". https://singulariki.com/roles/role-51-6091-00 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.
- O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
- BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
- BLS Employment Projections 2024–2034 U.S. Bureau of Labor Statistics
- Microsoft “Working with AI” working-with-ai Microsoft Research
- “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
- AI Occupational Exposure (AIOE) Felten, Raj & Seamans academic
- ILO / Gmyrek et al. GenAI exposure gradient 2025 International Labour Organization
- IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022 Institute for Structural Research (IBS)
- Frey & Osborne (2013) frey-osborne-automation academic
- Dingel & Neiman (2020) dingel-neiman-workathome academic
Data compiled June 2, 2026. Figures are estimates, not advice.
Cite this page
Plain
Singulariki. "Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers." 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-51-6091-00
APA
Singulariki. (2026). Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-51-6091-00
BibTeX
@misc{singulariki-role-51-6091-00,
title = {Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers},
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-51-6091-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.