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 →Riggers
Occupation · SOC 49-9096.00
Set up or repair rigging for construction projects, manufacturing plants, logging yards, ships and shipyards, or for the entertainment industry.
Also called: Machinery Erector · Machinery Mover · Motor Rigger · Rigger · Gantry Rigger · Hand Rigger · Heavy Lift Rigger · Marine Rigger · Rigging Fabricator · Ship Rigger · Acrobatic Rigger · Boat Rigger
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-49-9096-00/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.
- About average employment outlook (+3.2% by 2034)
- 2,500 openings/yr
- Moderate AI exposure
- Median pay $62,060/yr
- Work is mostly on-site (not teleworkable)
- 2013 flagged this for computerization (76th); today's AI task-overlap is comparatively low (22nd)
22nd-percentile task overlap — yet about 2,500 openings a year (+3.2% 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 | 10th | -1.2 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 3rd | 0.0 | |
| AI assistant applicability (Microsoft) Moderate | 61st | 0.2 |
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 · 76th 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 | About average · +3.2% by 2034 |
| Projected annual openings | 2,500 |
| Employment 2024 → 2034 | 24,600 → 25,400 |
“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.
Riggers sits at the 10th percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure eased from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
13% mean task exposure (2025)
10th percentile of 427 placed occupations
−2 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Riggers and Cable Splicers · 7215 | 13% | 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 14 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Test rigging to ensure safety and reliability. Core · importance 4.6
- Signal or verbally direct workers engaged in hoisting and moving loads to ensure safety of workers and materials. Core · importance 4.6
- Control movement of heavy equipment through narrow openings or confined spaces, using chainfalls, gin poles, gallows frames, and other equipment. Core · importance 4.3
- Select gear, such as cables, pulleys, and winches, according to load weights and sizes, facilities, and work schedules. Core · importance 4.3
- Tilt, dip, and turn suspended loads to maneuver over, under, or around obstacles, using multi-point suspension techniques. Core · importance 4.3
- Dismantle and store rigging equipment after use. Core · importance 4.2
- Attach loads to rigging to provide support or prepare them for moving, using hand and power tools. Core · importance 4.2
- Manipulate rigging lines, hoists, and pulling gear to move or support materials, such as heavy equipment, ships, or theatrical sets. Core · importance 4.2
- Align, level, and anchor machinery. Core · importance 4.2
- Install ground rigging for yarding lines, attaching chokers to logs and to the lines. Supplemental · importance 4.1
- Load machines onto trucks to prepare for transportation. Core · importance 4.0
- Attach pulleys and blocks to fixed overhead structures, such as beams, ceilings, and gin pole booms, using bolts and clamps. Core · importance 3.9
- Fabricate, set up, and repair rigging, supporting structures, hoists, and pulling gear, using hand and power tools. Core · importance 3.8
- Clean and dress machine surfaces and component parts. Core · importance 3.5
Work activities
- Communicate with coworkers to coordinate installations or repairs.
- Test mechanical systems to ensure proper functioning.
- Attach rigging to objects so they can be moved.
- Determine types of equipment, tools, or materials needed for jobs.
- Operate cranes, hoists, or other moving or lifting equipment.
- Move materials, equipment, or supplies.
- Align equipment or machinery.
- Dismantle heavy equipment or machinery.
- Clean equipment, parts, or tools to repair or maintain them in good working order.
- Load materials or equipment.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Abilities
| Problem Sensitivity | 3.8 | |
| Control Precision | 3.4 | |
| Multilimb Coordination | 3.4 | |
| Near Vision | 3.4 | |
| Depth Perception | 3.4 | |
| Oral Comprehension | 3.3 | |
| Information Ordering | 3.3 | |
| Trunk Strength | 3.3 | |
| Far Vision | 3.3 | |
| Visualization | 3.1 | |
| Manual Dexterity | 3.1 | |
| Reaction Time | 3.1 | |
| Static Strength | 3.1 | |
| Extent Flexibility | 3.1 | |
| Speech Recognition | 3.1 | |
| Written Comprehension | 3.0 | |
| Oral Expression | 3.0 | |
| Deductive Reasoning | 3.0 |
Knowledge
Transferable skills
| Operation and Control | 3.3 | |
| Coordination | 3.1 | |
| Operations Monitoring | 3.1 | |
| Complex Problem Solving | 3.0 | |
| Judgment and Decision Making | 3.0 | |
| Time Management | 3.0 |
Essential skills
| Reading Comprehension | 3.1 | |
| Active Listening | 3.1 | |
| Critical Thinking | 3.1 | |
| Monitoring | 3.1 | |
| Speaking | 3.0 |
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 | |
|---|---|---|
| Autodesk AutoCAD | Computer aided design CAD software | Hot technology |
| Microsoft Excel | Spreadsheet software | Hot technology |
| Microsoft Outlook | Electronic mail software | Hot technology |
| Microsoft Word | Word processing software | Hot technology |
| Autodesk Maya | Graphics or photo imaging 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 | 62.9% | |
| Less than a High School Diploma | 21.2% | |
| Post-Secondary Certificate | 16.0% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Career interests (Holland / RIASEC)
| Realistic | 7.0 | |
| Conventional | 3.6 | |
| Investigative | 2.4 | |
| Enterprising | 1.9 |
Interest areas
| Physical/Manual Labor | 6.1 | |
| Transportation/Machine Operation | 3.0 | |
| Engineering | 3.0 | |
| Mechanics/Electronics | 2.8 | |
| Construction/Woodwork | 2.5 | |
| Mathematics/Statistics | 1.5 |
Work styles
| Dependability | 3.0 | |
| Cautiousness | 2.6 | |
| Attention to Detail | 2.3 | |
| Stress Tolerance | 1.9 | |
| Integrity | 1.8 | |
| Perseverance | 1.7 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $38,930 |
| 25th percentile | $47,940 |
| Median (50th) | $62,060 |
| 75th percentile | $79,340 |
| 90th percentile | $100,480 |
| People employed | 24,190 |
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 |
|---|---|---|
| Construction · Sector | 7,130 | $64,330 |
| Manufacturing · Sector | 3,840 | $50,280 |
| Other Building Equipment Contractors · National industry | 2,740 | $61,800 |
| Mining, Quarrying, and Oil and Gas Extraction · Sector | 2,140 | $40,870 |
| Information · Sector | 1,760 | $101,840 |
| Transportation and Warehousing · Sector | 1,310 | $48,930 |
| Real Estate and Rental and Leasing · Sector | 1,310 | $59,040 |
| Arts, Entertainment, and Recreation · Sector | 830 | $68,450 |
| Temporary Help Services · National industry | 720 | $62,120 |
| Retail Trade · Sector | 700 | $43,080 |
| Professional, Scientific, and Technical Services · Sector | 600 | $79,330 |
| Electrical Contractors and Other Wiring Installation Contractors · National industry | 420 | $91,070 |
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 |
|---|---|---|
| Other Building Equipment Contractors · National industry | 113.73× | 2,740 |
| Mining, Quarrying, and Oil and Gas Extraction · Sector | 23.79× | 2,140 |
| Theater Companies and Dinner Theaters · National industry | 10.57× | 120 |
| Construction · Sector | 5.6× | 7,130 |
| Information · Sector | 3.86× | 1,760 |
| Real Estate and Rental and Leasing · Sector | 3.53× | 1,310 |
| Electrical Contractors and Other Wiring Installation Contractors · National industry | 2.5× | 420 |
| Arts, Entertainment, and Recreation · Sector | 2× | 830 |
Related occupations
Part of the Energy & Natural Resources career cluster.
- Hoist and Winch Operators · 53-7041.00 Compare →
- Crane and Tower Operators · 53-7021.00 Compare →
- Mobile Heavy Equipment Mechanics, Except Engines · 49-3042.00 Compare →
- Industrial Truck and Tractor Operators · 53-7051.00 Compare →
- Millwrights · 49-9044.00 Compare →
- Aircraft Structure, Surfaces, Rigging, and Systems Assemblers · 51-2011.00 Compare →
- Operating Engineers and Other Construction Equipment Operators · 47-2073.00 Compare →
- Structural Iron and Steel Workers · 47-2221.00 Compare →
- Maintenance Workers, Machinery · 49-9043.00 Compare →
- Structural Metal Fabricators and Fitters · 51-2041.00 Compare →
- Roustabouts, Oil and Gas · 47-5071.00 Compare →
- Pile Driver Operators · 47-2072.00 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 Riggers — 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 10th percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Riggers show 22nd-percentile AI task overlap — and about 2,500 annual U.S. openings
- Riggers rank in the 22nd 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 2,500 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 (+3.2%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $62,060, across about 24,190 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Riggers show 22nd-percentile AI task overlap — and about 2,500 annual U.S. openings • Riggers rank in the 22nd 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 2,500 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 (+3.2%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $62,060, across about 24,190 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Riggers". https://singulariki.com/roles/role-49-9096-00 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. "Riggers." 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-49-9096-00
APA
Singulariki. (2026). Riggers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-49-9096-00
BibTeX
@misc{singulariki-role-49-9096-00,
title = {Riggers},
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-49-9096-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.