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 →Shoe Machine Operators and Tenders
Occupation · SOC 51-6042.00
Operate or tend a variety of machines to join, decorate, reinforce, or finish shoes and shoe parts.
Also called: Boot Maker · Inseamer · Side Laster · Stitcher · Cobbler · Insole Department Worker · Shoe Cementer · Shoe Maker · Toe Trimmer · Anchor Operator · Anchorer · Ankle Patch Molder
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-51-6042-00/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 (-3.7% by 2034)
- 400 openings/yr
- Low AI exposure
- Median pay $38,160/yr
- Work is mostly on-site (not teleworkable)
- 2013 flagged this for computerization (94th); today's AI task-overlap is comparatively low (9th)
9th-percentile task overlap — yet about 400 openings a year (-3.7% 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 | 17th | -1.0 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 13th | 0.1 | |
| AI assistant applicability (Microsoft) Low | 10th | 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 1.0 · 94th 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 · -3.7% by 2034 |
| Projected annual openings | 400 |
| Employment 2024 → 2034 | 4,100 → 3,900 |
“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.
Shoe Machine Operators and Tenders sits at the 20th 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.
16% mean task exposure (2025)
20th percentile of 427 placed occupations
+3 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Shoemaking and Related Machine Operators · 8156 | 16% | 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 19 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Align parts to be stitched, following seams, edges, or markings, before positioning them under needles. Supplemental · importance 4.7
- Operate or tend machines to join, decorate, reinforce, or finish shoes and shoe parts. Supplemental · importance 4.5
- Remove and examine shoes, shoe parts, and designs to verify conformance to specifications such as proper embedding of stitches in channels. Core · importance 4.5
- Fill shuttle spools with thread from a machine's bobbin winder by pressing a foot treadle. Supplemental · importance 4.4
- Switch on machines, lower pressure feet or rollers to secure parts, and start machine stitching, using hand, foot, or knee controls. Supplemental · importance 4.4
- Staple sides of shoes, pressing a foot treadle to position and hold each shoe under the feeder of the machine. Supplemental · importance 4.4
- Draw thread through machine guide slots, needles, and presser feet in preparation for stitching, or load rolls of wire through machine axles. Supplemental · importance 4.3
- Study work orders or shoe part tags to obtain information about workloads, specifications, and the types of materials to be used. Core · importance 4.3
- Perform routine equipment maintenance such as cleaning and lubricating machines or replacing broken needles. Core · importance 4.3
- Position dies on material in a manner that will obtain the maximum number of parts from each portion of material. Supplemental · importance 4.2
- Test machinery to ensure proper functioning before beginning production. Supplemental · importance 4.2
- Collect shoe parts from conveyer belts or racks and place them in machinery such as ovens or on molds for dressing, returning them to conveyers or racks to send them to the next work station. Supplemental · importance 4.2
- Select and place spools of thread or pre-wound bobbins into shuttles, or onto spindles or loupers of stitching machines. Supplemental · importance 4.2
- Cut excess thread or material from shoe parts, using scissors or knives. Core · importance 4.1
- Turn setscrews on needle bars, and position required numbers of needles in stitching machines. Supplemental · importance 4.0
- Turn knobs to adjust stitch length and thread tension. Supplemental · importance 4.0
- Hammer loose staples for proper attachment. Supplemental · importance 3.6
- Load hot-melt plastic rod glue through reactivator axles, using wrenches, and switch on reactivators, setting temperature and timers to heat glue to specifications. Supplemental
- Turn screws to regulate size of staples. Supplemental
Work activities
- Read work orders or other instructions to determine product specifications or materials requirements.
- Remove products or workpieces from production equipment.
- Clean production equipment.
- Replace worn equipment components.
- Maintain production or processing equipment.
- Trim excess material from workpieces.
- Operate sewing equipment.
- Align parts or workpieces to ensure proper assembly.
- Mount materials or workpieces onto production equipment.
- Mount attachments or tools onto production equipment.
- Conduct test runs of production equipment.
- Load materials into production equipment.
- Feed materials or products into or through equipment.
- Assemble garments or textile products.
- Select production input materials.
- Inspect products or operations to ensure that standards are met.
- Inspect finishes of workpieces or finished products.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Abilities
| Near Vision | 3.9 | |
| Arm-Hand Steadiness | 3.6 | |
| Control Precision | 3.6 | |
| Manual Dexterity | 3.1 | |
| Finger Dexterity | 3.1 | |
| Oral Expression | 3.0 | |
| Problem Sensitivity | 3.0 | |
| Perceptual Speed | 3.0 | |
| Selective Attention | 3.0 | |
| Multilimb Coordination | 3.0 | |
| Reaction Time | 3.0 | |
| Extent Flexibility | 3.0 | |
| Oral Comprehension | 2.9 | |
| Category Flexibility | 2.9 | |
| Visualization | 2.9 | |
| Rate Control | 2.9 | |
| Trunk Strength | 2.9 | |
| Far Vision | 2.9 | |
| Speech Recognition | 2.9 | |
| Written Comprehension | 2.8 | |
| Deductive Reasoning | 2.8 | |
| Inductive Reasoning | 2.8 | |
| Information Ordering | 2.8 | |
| Visual Color Discrimination | 2.8 | |
| Speech Clarity | 2.8 |
Knowledge
| Production and Processing | 3.3 | |
| Administration and Management | 3.0 | |
| Education and Training | 2.7 |
Essential skills
| Reading Comprehension | 3.1 | |
| Active Listening | 3.0 | |
| Critical Thinking | 3.0 | |
| Monitoring | 3.0 | |
| Speaking | 2.8 |
Transferable skills
| Operations Monitoring | 3.0 | |
| Operation and Control | 3.0 | |
| Equipment Maintenance | 2.9 | |
| Quality Control Analysis | 2.9 | |
| Troubleshooting | 2.8 | |
| Time Management | 2.8 | |
| Complex Problem Solving | 2.6 |
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 | |
|---|---|---|
| Adobe Acrobat | Document management software | Hot technology |
| 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 |
| Inventory tracking software | Inventory management software | |
| Production control 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.
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 | 38.0% | |
| Some College Courses | 15.3% | |
| Associate's Degree (or other 2-year degree) | 0.3% |
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 | 3.9 | |
| Investigative | 1.8 | |
| Artistic | 1.7 | |
| Enterprising | 1.5 | |
| Social | 1.3 |
Interest areas
| Physical/Manual Labor | 3.2 | |
| Mechanics/Electronics | 2.3 | |
| Transportation/Machine Operation | 1.5 | |
| Engineering | 1.5 | |
| Construction/Woodwork | 1.5 | |
| Applied Arts and Design | 1.3 | |
| Management/Administration | 1.2 |
Work styles
| Attention to Detail | 2.3 | |
| Dependability | 2.1 | |
| Cautiousness | 1.6 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $24,160 |
| 25th percentile | $30,450 |
| Median (50th) | $38,160 |
| 75th percentile | $43,390 |
| 90th percentile | $47,860 |
| People employed | 3,270 |
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 | 3,230 | $38,160 |
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 | 11.93× | 3,230 |
Related occupations
Part of the Advanced Manufacturing career cluster.
- Sewing Machine Operators · 51-6031.00 Compare →
- Cutting and Slicing Machine Setters, Operators, and Tenders · 51-9032.00 Compare →
- Shoe and Leather Workers and Repairers · 51-6041.00 Compare →
- Grinding and Polishing Workers, Hand · 51-9022.00 Compare →
- Cutters and Trimmers, Hand · 51-9031.00 Compare →
- Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic · 51-4033.00 Compare →
- Woodworking Machine Setters, Operators, and Tenders, Except Sawing · 51-7042.00 Compare →
- Textile Cutting Machine Setters, Operators, and Tenders · 51-6062.00 Compare →
- Adhesive Bonding Machine Operators and Tenders · 51-9191.00 Compare →
- Sawing Machine Setters, Operators, and Tenders, Wood · 51-7041.00 Compare →
- Sewers, Hand · 51-6051.00 Compare →
- Pressers, Textile, Garment, and Related Materials · 51-6021.00 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 Shoe Machine Operators and Tenders — 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.
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
- Precision Production · field of study
Zoom out
On the global GenAI exposure gradient this work sits around the 20th percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Shoe Machine Operators and Tenders show 9th-percentile AI task overlap — and about 400 annual U.S. openings
- Shoe Machine Operators and Tenders rank in the 9th 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 400 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 (-3.7%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $38,160, across about 3,270 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Shoe Machine Operators and Tenders show 9th-percentile AI task overlap — and about 400 annual U.S. openings • Shoe Machine Operators and Tenders rank in the 9th 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 400 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 (-3.7%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $38,160, across about 3,270 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Shoe Machine Operators and Tenders". https://singulariki.com/roles/role-51-6042-00 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. "Shoe Machine Operators and Tenders." 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-51-6042-00
APA
Singulariki. (2026). Shoe Machine Operators and Tenders. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-51-6042-00
BibTeX
@misc{singulariki-role-51-6042-00,
title = {Shoe Machine Operators and Tenders},
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-51-6042-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.