# Mobile Heavy Equipment Mechanics, Except Engines

> Diagnose, adjust, repair, or overhaul mobile mechanical, hydraulic, and pneumatic equipment, such as cranes, bulldozers, graders, and conveyors, used in construction, logging, and mining.

- **SOC code:** 49-3042.00
- **Canonical URL:** https://singulariki.com/roles/role-49-3042-00
- **Also known as:** Equipment Mechanic, Heavy Equipment Mechanic, Heavy Equipment Technician, Mechanic, Construction Equipment Mechanic, Equipment Technician, Field Mechanic, Field Service Technician
- **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):
- Repair and replace damaged or worn parts.
- Test mechanical products and equipment after repair or assembly to ensure proper performance and compliance with manufacturers' specifications.
- Operate and inspect machines or heavy equipment to diagnose defects.
- Read and understand operating manuals, blueprints, and technical drawings.
- Dismantle and reassemble heavy equipment using hoists and hand tools.
- Overhaul and test machines or equipment to ensure operating efficiency.
- Adjust, maintain, and repair or replace subassemblies, such as transmissions and crawler heads, using hand tools, jacks, and cranes.
- Repair, rewire, and troubleshoot electrical systems.
- Diagnose faults or malfunctions to determine required repairs, using engine diagnostic equipment such as computerized test equipment and calibration devices.
- Examine parts for damage or excessive wear, using micrometers and gauges.
- Weld or solder broken parts and structural members, using electric or gas welders and soldering tools.
- Research, order, and maintain parts inventory for services and repairs.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Mechanical _(knowledge)_
- Troubleshooting _(transferable_skill)_
- Repairing _(transferable_skill)_
- Equipment Maintenance _(transferable_skill)_
- Manual Dexterity _(ability)_
- Control Precision _(ability)_
- Finger Dexterity _(ability)_
- Extent Flexibility _(ability)_
- Near Vision _(ability)_
- Operations Monitoring _(transferable_skill)_
- Problem Sensitivity _(ability)_
- Arm-Hand Steadiness _(ability)_

**Skills in demand:**
- Equipment Maintenance _(Specialized Skill)_
- Finger Dexterity _(Common Skill)_
- Visualization _(Specialized Skill)_
- Information Ordering _(Specialized Skill)_
- Equipment Selection _(Specialized Skill)_
- Deductive Reasoning _(Common Skill)_
- Mathematics _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Complex Problem Solving _(Common Skill)_
- Writing _(Common Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft Word _(hot technology)_
- Database software
- Fleet management software
- Maintenance management software
- Recordkeeping software

## AI exposure & outlook

- **AI task-overlap index:** 15th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 12th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 20th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 22nd percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 44th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 5.8% growth (About average); 16.5k annual openings; 188.7k → 199.6k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $63,980; 180,270 employed.

## How people actually use AI here

Anthropic Economic Index — measured AI conversations mapped to this occupation's tasks:

- **Automation vs augmentation:** 55% automation, 36% augmentation (usage-weighted).
- **Autonomy median:** 4.0 (higher = AI acts more independently).
- **Dominant collaboration mode:** learning.

**Tasks most handed to AI here:**
- Diagnose faults or malfunctions to determine required repairs, using engine diagnostic equipment such as computerized test equipment and calibration devices. _(0.6% of measured AI use; learning)_

**Example prompts (honest phrasings of the tasks above — starting points, not endorsed instructions):**
- Help me diagnose faults or malfunctions to determine required repairs, using engine diagnostic equipment such as computerized test equipment and calibration devices.

## 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/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **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-49-3042-00_