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 →Coin, Vending, and Amusement Machine Servicers and Repairers
Occupation · SOC 49-9091.00
Install, service, adjust, or repair coin, vending, or amusement machines including video games, juke boxes, pinball machines, or slot machines.
Also called: Field Service Technician · Service Technician · Slot Technician · Vending Technician · Cooler Deliverer · Fountain Vending Mechanic · Full Service Vending Driver · Refurbish Technician · Vending Mechanic · Vending Service Technician · ATM Mechanic (Automatic Teller Machine Mechanic) · ATM Technician (Automated Teller Machine Technician)
Job family: Installation, Maintenance, and Repair Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-49-9091-00/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 (-2.9% by 2034)
- 3,500 openings/yr
- Low AI exposure
- Median pay $47,350/yr
- Can largely be done remotely (teleworkable)
- 2013 flagged this for computerization (86th); today's AI task-overlap is comparatively low (28th)
28th-percentile task overlap — yet about 3,500 openings a year (-2.9% 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 | 28th | -0.7 | |
| LLM task exposure, γ (OpenAI / Eloundou) Moderate | 34th | 0.3 | |
| AI assistant applicability (Microsoft) Low | 24th | 0.1 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.3), with simple added tooling (β 0.3), and including AI-powered software (γ 0.3). 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.
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 · 86th 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.
| Refer to manuals and wiring diagrams to gather information needed to repair machines. | 0.3% |
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 · -2.9% by 2034 |
| Projected annual openings | 3,500 |
| Employment 2024 → 2034 | 32,500 → 31,600 |
“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.
Coin, Vending, and Amusement Machine Servicers and Repairers sits at the 72nd 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.
38% mean task exposure (2025)
72nd percentile of 427 placed occupations
+2 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Meter Readers and Vending-machine Collectors · 9623 | 38% | Gradient 1 |
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.
- Keep records of merchandise distributed and money collected. Supplemental · importance 4.5
- Fill machines with products, ingredients, money, and other supplies. Core · importance 4.4
- Inspect machines and meters to determine causes of malfunctions and fix minor problems such as jammed bills or stuck products. Core · importance 4.3
- Collect coins and bills from machines, prepare invoices, and settle accounts with concessionaires. Supplemental · importance 4.3
- Test machines to determine proper functioning. Core · importance 4.2
- Make service calls to maintain and repair machines. Supplemental · importance 4.1
- Replace malfunctioning parts, such as worn magnetic heads on automatic teller machine (ATM) card readers. Core · importance 4.1
- Adjust machine pressure gauges and thermostats. Supplemental · importance 4.1
- Maintain records of machine maintenance and repair. Core · importance 4.0
- Clean and oil machine parts. Core · importance 4.0
- Order parts needed for machine repairs. Core · importance 3.9
- Adjust and repair coin, vending, or amusement machines and meters and replace defective mechanical and electrical parts, using hand tools, soldering irons, and diagrams. Core · importance 3.8
- Record transaction information on forms or logs, and notify designated personnel of discrepancies. Core · importance 3.8
- Disassemble and assemble machines, according to specifications and using hand and power tools. Supplemental · importance 3.7
- Contact other repair personnel or make arrangements for the removal of machines in cases where major repairs are required. Supplemental · importance 3.7
- Transport machines to installation sites. Supplemental · importance 3.5
- Refer to manuals and wiring diagrams to gather information needed to repair machines. Supplemental · importance 3.5
- Install machines, making the necessary water and electrical connections in compliance with codes. Supplemental · importance 3.4
- Prepare repair cost estimates. Supplemental
Work activities
- Maintain work equipment or machinery.
- Document operational activities.
- Collect payments for goods or services.
- Travel to work sites to perform installation, repair or maintenance work.
- Inspect mechanical equipment to locate damage, defects, or wear.
- Test mechanical equipment to ensure proper functioning.
- Confer with coworkers to resolve equipment problems.
- Clean equipment, parts, or tools to repair or maintain them in good working order.
- Lubricate equipment to allow proper functioning.
- Adjust equipment to ensure optimal performance.
- Maintain repair or maintenance records.
- Replace worn, damaged, or defective mechanical parts.
- Repair worn, damaged, or defective mechanical parts.
- Order materials, supplies, or equipment.
- Assemble mechanical components or machine parts.
- Dismantle heavy equipment or machinery.
- Install home appliances.
- Read technical information needed to perform maintenance or repairs.
- Drive trucks or other vehicles to or at work sites.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Knowledge
| Computers and Electronics | 4.0 | |
| Mechanical | 3.5 | |
| Customer and Personal Service | 3.5 | |
| English Language | 3.0 |
Abilities
| Manual Dexterity | 3.6 | |
| Finger Dexterity | 3.6 | |
| Arm-Hand Steadiness | 3.4 | |
| Control Precision | 3.4 | |
| Near Vision | 3.4 | |
| Information Ordering | 3.3 | |
| Multilimb Coordination | 3.3 | |
| Oral Comprehension | 3.1 | |
| Oral Expression | 3.1 | |
| Problem Sensitivity | 3.1 | |
| Deductive Reasoning | 3.1 | |
| Inductive Reasoning | 3.1 | |
| Visualization | 3.1 | |
| Written Comprehension | 3.0 | |
| Selective Attention | 3.0 | |
| Flexibility of Closure | 2.9 | |
| Far Vision | 2.9 | |
| Speech Recognition | 2.9 | |
| Speech Clarity | 2.9 |
Transferable skills
| Repairing | 3.5 | |
| Equipment Maintenance | 3.4 | |
| Troubleshooting | 3.3 | |
| Operations Monitoring | 3.0 | |
| Operation and Control | 3.0 | |
| Quality Control Analysis | 3.0 | |
| Service Orientation | 2.9 | |
| Complex Problem Solving | 2.9 | |
| Judgment and Decision Making | 2.9 | |
| Systems Analysis | 2.9 | |
| Time Management | 2.9 | |
| Systems Evaluation | 2.8 |
Essential skills
| Reading Comprehension | 2.9 | |
| Active Listening | 2.9 | |
| Speaking | 2.9 | |
| Critical Thinking | 2.9 | |
| Monitoring | 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 Outlook | Electronic mail software | Hot technology |
| Microsoft PowerPoint | Presentation software | Hot technology |
| Microsoft Word | Word processing software | Hot technology |
| Email software | Electronic mail software | |
| Inventory tracking software | Inventory management software | |
| Web browser software | Internet browser 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 | 91.6% | |
| Post-Secondary Certificate | 7.6% | |
| Associate's Degree (or other 2-year degree) | 0.8% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Career interests (Holland / RIASEC)
| Realistic | 6.1 | |
| Conventional | 5.7 | |
| Enterprising | 1.9 | |
| Investigative | 1.8 |
Interest areas
| Mechanics/Electronics | 6.1 | |
| Physical/Manual Labor | 3.7 | |
| Engineering | 3.0 | |
| Information Technology | 1.9 | |
| Accounting | 1.9 | |
| Transportation/Machine Operation | 1.7 | |
| Office Work | 1.5 | |
| Personal Service | 1.4 |
Work styles
| Dependability | 2.2 | |
| Attention to Detail | 2.1 | |
| Integrity | 1.5 | |
| Cautiousness | 1.4 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $31,420 |
| 25th percentile | $38,580 |
| Median (50th) | $47,350 |
| 75th percentile | $56,290 |
| 90th percentile | $64,720 |
| People employed | 28,260 |
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 |
|---|---|---|
| Arts, Entertainment, and Recreation · Sector | 8,720 | $45,010 |
| Retail Trade · Sector | 4,960 | $39,440 |
| Accommodation and Food Services · Sector | 4,790 | $50,580 |
| Wholesale Trade · Sector | 3,970 | $49,780 |
| Casino Hotels · National industry | 3,130 | $47,360 |
| Other Services (except Public Administration) · Sector | 1,570 | $50,140 |
| Manufacturing · Sector | 1,510 | $53,810 |
| Real Estate and Rental and Leasing · Sector | 900 | $50,340 |
| Transportation and Warehousing · Sector | 740 | $58,290 |
| Construction · Sector | 160 | $48,690 |
| Information · Sector | 130 | $52,930 |
| Management of Companies and Enterprises · Sector | 110 | $54,150 |
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 |
|---|---|---|
| Casino Hotels · National industry | 50.67× | 3,130 |
| Arts, Entertainment, and Recreation · Sector | 18.01× | 8,720 |
| Wholesale Trade · Sector | 3.59× | 3,970 |
| Real Estate and Rental and Leasing · Sector | 2.07× | 900 |
| Other Services (except Public Administration) · Sector | 1.94× | 1,570 |
| Accommodation and Food Services · Sector | 1.84× | 4,790 |
| Retail Trade · Sector | 1.74× | 4,960 |
| Manufacturing · Sector | 0.65× | 1,510 |
Related occupations
Part of the Hospitality, Events, & Tourism career cluster.
- Computer, Automated Teller, and Office Machine Repairers · 49-2011.00 Compare →
- Paper Goods Machine Setters, Operators, and Tenders · 51-9196.00 Compare →
- Industrial Machinery Mechanics · 49-9041.00 Compare →
- Maintenance Workers, Machinery · 49-9043.00 Compare →
- Packaging and Filling Machine Operators and Tenders · 51-9111.00 Compare →
- Machine Feeders and Offbearers · 53-7063.00 Compare →
- Watch and Clock Repairers · 49-9064.00 Compare →
- Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders · 51-9041.00 Compare →
- Office Machine Operators, Except Computer · 43-9071.00 Compare →
- Home Appliance Repairers · 49-9031.00 Compare →
- Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic · 51-4035.00 Compare →
- Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers · 51-6091.00 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 Coin, Vending, and Amusement Machine Servicers and Repairers — 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
Zoom out
On the global GenAI exposure gradient this work sits around the 72nd percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Coin, Vending, and Amusement Machine Servicers and Repairers show 28th-percentile AI task overlap — and about 3,500 annual U.S. openings
- Coin, Vending, and Amusement Machine Servicers and Repairers rank in the 28th 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 3,500 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 declining (-2.9%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $47,350, across about 28,260 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Coin, Vending, and Amusement Machine Servicers and Repairers show 28th-percentile AI task overlap — and about 3,500 annual U.S. openings • Coin, Vending, and Amusement Machine Servicers and Repairers rank in the 28th 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 3,500 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 declining (-2.9%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $47,350, across about 28,260 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Coin, Vending, and Amusement Machine Servicers and Repairers". https://singulariki.com/roles/role-49-9091-00 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
- Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
- 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. "Coin, Vending, and Amusement Machine Servicers and Repairers." 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-49-9091-00
APA
Singulariki. (2026). Coin, Vending, and Amusement Machine Servicers and Repairers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-49-9091-00
BibTeX
@misc{singulariki-role-49-9091-00,
title = {Coin, Vending, and Amusement Machine Servicers and Repairers},
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-49-9091-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.