JobShiftAtlas
Source-backed profile

Mobile Heavy Equipment Mechanics

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

BLS SOC 49-3042 · O*NET-SOC 49-3042.00 · BLS 2025–2035 · O*NET 31.0

Median annual wage, 2025

$65,510

Projected change, 2025–2035

6.7%

2025–2035 annual average openings

15,000

AI exposure

Low

Editorial analysis

Predictive maintenance could change when the mechanic gets the call

Heavy equipment increasingly reports its own operating condition. Engines, hydraulic systems, transmissions, emissions equipment, and electrical systems can generate data that helps identify abnormal temperatures, pressures, vibration, fuel use, and performance.

AI can turn those readings into maintenance warnings before a failure shuts down an expensive machine. For a mine, farm, construction company, or equipment fleet, avoiding one major breakdown can be worth far more than the cost of the monitoring technology.

The repair environment remains stubbornly physical

Heavy-equipment mechanics often work around large components, hydraulic lines, dirt, vibration, weather, awkward access, and machines that cannot simply be brought into a clean laboratory. A diagnostic recommendation is useful, but someone must safely disassemble, inspect, measure, repair, and test the machine.

  • Condition monitoring can make maintenance more proactive.
  • AI can help mechanics search service information and interpret complex fault histories.
  • Remote diagnostics may allow experts to support technicians in the field.
  • Hydraulics, diesel systems, electrical troubleshooting, welding, and physical repair remain valuable technical skills.

Labor-market picture

Two measures, different questions

These measures offer two different ways to understand the occupation: BLS projections show how national employment may change over time, while relative AI exposure highlights where tasks may intersect with AI-related capabilities. Read them with the work profile and your local context to decide what deserves closer investigation.

Employment

Employment, 2025
180,300 jobs
Projected employment, 2035
192,500 jobs
Projected change, 20252035
12,100 jobs (6.7%)

Relative AI exposure

BLS category: Low. This is a relative task-exposure classification, not a forecast of employment change, automation, wages, or worker replacement.

Relative AI exposure · Low

Work profile

Representative tasks

These task statements come from O*NET 31.0. They are source facts, not labels assigned by the AI exposure measure.

  • 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.

Skills that matter

Skills in the source profile

Importance uses O*NET's 1–5 scale. Level uses its 0–7 scale. Select a skill to see its source definition and reported values.

Technology and tools

These technology records come from O*NET and show software or tools reported in the occupation profile. They describe work context, not a required checklist for every employer.

  • Data base user interface and query software
  • Materials requirements planning logistics and supply chain software
  • Facilities management software
  • Spreadsheet software
  • Office suite software
  • Electronic mail software
  • Word processing software

Preparation

Typical entry education
High school diploma or equivalent
Related experience
None
On-the-job training
Long-term on-the-job training

Ask JobShift

Ask Dr. Morgan Reed about this evidence

Morgan is the JobShift Atlas analyst powered by AI. Answers draw from the evidence on this page and explain what it can tell you.

Source provenance

Labor-market and exposure facts: bls-ep-2025-35. Work profile: onet-31.0. Verify release details on the Sources page.