# Riggers

> Set up or repair rigging for construction projects, manufacturing plants, logging yards, ships and shipyards, or for the entertainment industry.

- **SOC code:** 49-9096.00
- **Canonical URL:** https://singulariki.com/roles/role-49-9096-00
- **Also known as:** Machinery Erector, Machinery Mover, Motor Rigger, Rigger, Gantry Rigger, Hand Rigger, Heavy Lift Rigger, Marine Rigger
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Test rigging to ensure safety and reliability.
- Signal or verbally direct workers engaged in hoisting and moving loads to ensure safety of workers and materials.
- Control movement of heavy equipment through narrow openings or confined spaces, using chainfalls, gin poles, gallows frames, and other equipment.
- Select gear, such as cables, pulleys, and winches, according to load weights and sizes, facilities, and work schedules.
- Tilt, dip, and turn suspended loads to maneuver over, under, or around obstacles, using multi-point suspension techniques.
- Dismantle and store rigging equipment after use.
- Attach loads to rigging to provide support or prepare them for moving, using hand and power tools.
- Manipulate rigging lines, hoists, and pulling gear to move or support materials, such as heavy equipment, ships, or theatrical sets.
- Align, level, and anchor machinery.
- Install ground rigging for yarding lines, attaching chokers to logs and to the lines.
- Load machines onto trucks to prepare for transportation.
- Attach pulleys and blocks to fixed overhead structures, such as beams, ceilings, and gin pole booms, using bolts and clamps.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Problem Sensitivity _(ability)_
- Mechanical _(knowledge)_
- Public Safety and Security _(knowledge)_
- Production and Processing _(knowledge)_
- Customer and Personal Service _(knowledge)_
- Administration and Management _(knowledge)_
- Control Precision _(ability)_
- Multilimb Coordination _(ability)_
- Near Vision _(ability)_
- Depth Perception _(ability)_
- Design _(knowledge)_
- English Language _(knowledge)_

**Skills in demand:**
- Depth Perception _(Common Skill)_
- English Language _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Mathematics _(Common Skill)_
- Visualization _(Specialized Skill)_
- Speech Recognition _(Specialized Skill)_
- Reading Comprehension _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Active Listening _(Common Skill)_
- Time Management _(Common Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft Outlook _(Common Skill)_

**Tools & technology:**
- Autodesk AutoCAD _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft Word _(hot technology)_
- Autodesk Maya

## AI exposure & outlook

- **AI task-overlap index:** 22nd percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 10th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 3rd percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 61st percentile (Moderate) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 76th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 3.2% growth (About average); 2.5k annual openings; 24.6k → 25.4k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $62,060; 24,190 employed.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-49-9096-00_