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 →Occupation · SOC 47-5044.00
Operate underground loading or moving machine to load or move coal, ore, or rock using shuttle or mine car or conveyors. Equipment may include power shovels, hoisting engines equipped with cable-drawn scraper or scoop, or machines equipped with gathering arms and conveyor.
Also called: Load Haul Dump Operator (LHD Operator) · Loader Operator · Loading Machine Operator · Shuttle Car Operator · Coal Hauler Operator · Equipment Operator · Miner Operator · Production Miner · Ram Car Operator · Underground Miner · Buggy Driver · Buggy Man
Job family: Construction and Extraction Occupations
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-47-5044-00/context.md directly.
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.
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Independent published positions, read together — not a forecast.
4th-percentile task overlap — yet about 500 openings a year (-22.3% projected, BLS) . What exposure means →
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.
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 |
|---|---|---|---|
| LLM task exposure, γ (OpenAI / Eloundou) Low | 5th | 0.0 | |
| AI assistant applicability (Microsoft) Low | 10th | 0.0 |
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.
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Declining · -22.3% by 2034 |
| Projected annual openings | 500 |
| Employment 2024 → 2034 | 6,400 → 5,000 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
All 28 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| Control Precision | 3.8 | |
| Multilimb Coordination | 3.4 | |
| Reaction Time | 3.4 | |
| Problem Sensitivity | 3.3 | |
| Arm-Hand Steadiness | 3.3 | |
| Manual Dexterity | 3.3 | |
| Rate Control | 3.3 | |
| Near Vision | 3.3 | |
| Perceptual Speed | 3.1 | |
| Selective Attention | 3.1 | |
| Time Sharing | 3.1 | |
| Trunk Strength | 3.1 | |
| Depth Perception | 3.1 | |
| Oral Comprehension | 3.0 | |
| Oral Expression | 3.0 | |
| Information Ordering | 3.0 | |
| Category Flexibility | 3.0 | |
| Visualization | 3.0 | |
| Extent Flexibility | 3.0 | |
| Far Vision | 3.0 | |
| Auditory Attention | 3.0 | |
| Speech Recognition | 3.0 | |
| Speech Clarity | 3.0 | |
| Deductive Reasoning | 2.9 | |
| Inductive Reasoning | 2.9 | |
| Finger Dexterity | 2.9 | |
| Visual Color Discrimination | 2.9 |
| Operation and Control | 3.4 | |
| Operations Monitoring | 3.3 | |
| Troubleshooting | 3.1 | |
| Coordination | 3.0 | |
| Equipment Maintenance | 3.0 | |
| Social Perceptiveness | 2.9 |
| Mechanical | 3.3 | |
| Education and Training | 3.0 |
| Active Listening | 3.0 | |
| Speaking | 3.0 | |
| Critical Thinking | 3.0 | |
| Reading Comprehension | 2.9 | |
| Monitoring | 2.9 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
| Example | Category | |
|---|---|---|
| Microsoft Excel | Spreadsheet software | Hot technology |
| Microsoft Outlook | Electronic mail software | Hot technology |
| Microsoft PowerPoint | Presentation software | Hot technology |
| Microsoft Windows | Operating system software | Hot technology |
| Automated systems software | Industrial control software | |
| Inventory management systems | Inventory management software | |
| Maintenance management software | Facilities management software | |
| Mine maintenance software | Facilities management software | |
| Work time accounting software | Time accounting software |
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.
Share of people in this occupation at each level of education.
| High School Diploma | 52.6% | |
| Less than a High School Diploma | 40.0% | |
| Post-Secondary Certificate | 6.2% | |
| Associate's Degree (or other 2-year degree) | 1.3% |
The interests and personal qualities O*NET associates with people who do this work.
| Realistic | 7.0 | |
| Conventional | 3.7 | |
| Investigative | 2.1 |
| Physical/Manual Labor | 6.2 | |
| Transportation/Machine Operation | 6.1 | |
| Mechanics/Electronics | 4.3 | |
| Engineering | 2.5 | |
| Construction/Woodwork | 1.2 | |
| Physical Science | 1.1 |
| Dependability | 3.0 | |
| Cautiousness | 2.3 | |
| Stress Tolerance | 2.1 | |
| Attention to Detail | 1.9 | |
| Perseverance | 1.5 | |
| Integrity | 1.4 | |
| Self-Control | 1.2 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $48,310 |
| 25th percentile | $59,130 |
| Median (50th) | $68,860 |
| 75th percentile | $76,820 |
| 90th percentile | $82,900 |
| People employed | 6,130 |
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 |
|---|---|---|
| Mining, Quarrying, and Oil and Gas Extraction · Sector | 5,650 | $70,800 |
| Construction · Sector | 150 | $47,760 |
| Manufacturing · Sector | 100 | $46,840 |
| Utilities · Sector | — | $94,010 |
| Fossil Fuel Electric Power Generation · National industry | — | $94,010 |
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 |
|---|---|---|
| Mining, Quarrying, and Oil and Gas Extraction · Sector | 247.81× | 5,650 |
| Construction · Sector | 0.46× | 150 |
| Manufacturing · Sector | 0.2× | 100 |
Part of the Energy & Natural Resources career cluster.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
Options the data surfaces for Loading and Moving Machine Operators, Underground Mining — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Capabilities this work builds that are used across many other occupations.
Occupations O*NET rates as related — the nearby moves on the map.
How people typically prepare for this work.
See where this work sits in the bigger picture.
Loading and Moving Machine Operators, Underground Mining show 4th-percentile AI task overlap — and about 500 annual U.S. openings
Loading and Moving Machine Operators, Underground Mining show 4th-percentile AI task overlap — and about 500 annual U.S. openings • Loading and Moving Machine Operators, Underground Mining rank in the 4th 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 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 (-22.3%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $68,860, across about 6,130 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Loading and Moving Machine Operators, Underground Mining". https://singulariki.com/roles/role-47-5044-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
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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.
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.
Data compiled June 2, 2026. Figures are estimates, not advice.
Singulariki. "Loading and Moving Machine Operators, Underground Mining." 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. Accessed June 7, 2026. https://singulariki.com/roles/role-47-5044-00
Singulariki. (2026). Loading and Moving Machine Operators, Underground Mining. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-47-5044-00
@misc{singulariki-role-47-5044-00,
title = {Loading and Moving Machine Operators, Underground Mining},
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. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-47-5044-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.