Cleaners of Vehicles and Equipment

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.

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.1), and including AI-powered software (γ 0.1). 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.

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

“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 2 occupations below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.

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 21 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

• Rinse objects and place them on drying racks or use cloth, squeegees, or air compressors to dry surfaces. Core · importance 4.0
• Apply paints, dyes, polishes, reconditioners, waxes, or masking materials to vehicles to preserve, protect, or restore color or condition. Core · importance 4.0
• Clean and polish vehicle windows. Core · importance 3.9
• Drive vehicles to or from workshops or customers' workplaces or homes. Core · importance 3.9
• Scrub, scrape, or spray machine parts, equipment, or vehicles, using scrapers, brushes, clothes, cleaners, disinfectants, insecticides, acid, abrasives, vacuums, or hoses. Core · importance 3.9
• Clean the plastic work inside cars, using paintbrushes. Supplemental · importance 3.9
• Inspect parts, equipment, or vehicles for cleanliness, damage, and compliance with standards or regulations. Core · importance 3.9
• Mix cleaning solutions, abrasive compositions, or other compounds, according to formulas. Core · importance 3.7
• Disassemble and reassemble machines or equipment or remove and reattach vehicle parts or trim, using hand tools. Supplemental · importance 3.7
• Maintain inventories of supplies. Core · importance 3.7
• Pre-soak or rinse machine parts, equipment, or vehicles by immersing objects in cleaning solutions or water, manually or using hoists. Core · importance 3.7
• Turn valves or disconnect hoses to eliminate water, cleaning solutions, or vapors from machinery or tanks. Core · importance 3.6
• Turn valves or handles on equipment to regulate pressure or flow of water, air, steam, or abrasives from sprayer nozzles. Core · importance 3.6
• Monitor operation of cleaning machines and stop machines or notify supervisors when malfunctions occur. Core · importance 3.6
• Sweep, shovel, or vacuum loose debris or salvageable scrap into containers and remove containers from work areas. Core · importance 3.6
• Lubricate machinery, vehicles, or equipment or perform minor repairs or adjustments, using hand tools. Supplemental · importance 3.5
• Press buttons to activate cleaning equipment or machines. Core · importance 3.5
• Connect hoses or lines to pumps or other equipment. Core · importance 3.4
• Transport materials, equipment, or supplies to or from work areas, using carts or hoists. Supplemental · importance 3.3
• Fit boot spoilers, side skirts, or mud flaps to cars. Supplemental · importance 2.8
• Collect and test samples of cleaning solutions or vapors. Supplemental

Work activities

• Inspect motor vehicles.
• Clean machinery or equipment.
• Clean vehicles or vehicle components.
• Mix substances or compounds needed for work activities.
• Operate industrial equipment.
• Drive passenger vehicles.
• Control pumps or pumping equipment.
• Maintain vehicles in good working condition.
• Monitor engine operation or functioning.
• Report vehicle or equipment malfunctions.
• Install parts, assemblies, or attachments in transportation or material handling equipment.
• Connect hoses to equipment or machinery.
• Acquire supplies or equipment.
• Apply protective or decorative finishes to workpieces or products.
• Clean facilities or work areas.
• Remove debris or damaged materials.
• Shovel materials.
• Move materials, equipment, or supplies.

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Abilities

Knowledge

Transferable skills

Essential skills

Skills in demand

Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.

• English Language Common Skill
• Microsoft Windows Common Skill
• Finger Dexterity Common Skill
• Deductive Reasoning Common Skill
• Time Management Common Skill
• Speech Recognition Specialized Skill
• Information Ordering Specialized Skill
• Inductive Reasoning Common Skill
• Active Listening Common Skill
• Mathematics Common Skill
• Chemistry Specialized Skill
• Depth Perception Common Skill
• Visualization Specialized Skill
• Systems Analysis Specialized Skill
• Reading Comprehension Common Skill
• Equipment Selection Specialized Skill
• Equipment Maintenance Specialized Skill
• Critical Thinking Common Skill
• Psychology Specialized Skill
• Social Perceptiveness Common Skill
• Complex Problem Solving Common Skill
• Foreign Language Common Skill
• Negotiation Common Skill
• Instructing Specialized Skill
• Active Learning Common Skill
• Writing Common Skill
• Learning Strategies Specialized Skill
• Physics Specialized Skill
• Telecommunications Specialized Skill
• Geography Specialized Skill
• Biology Specialized Skill
• Installation Specialized Skill

Tools & technology

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

No formal educational credential · 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.

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Career interests (Holland / RIASEC)

Interest areas

Work styles

Wages & employment

U.S. · annual wages (BLS OEWS)

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.

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).

Related occupations

Part of the Supply Chain & Transportation career cluster.

• Laundry and Dry-Cleaning Workers · 51-6011.00 Compare →
• Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders · 51-9192.00 Compare →
• Janitors and Cleaners, Except Maids and Housekeeping Cleaners · 37-2011.00 Compare →
• Septic Tank Servicers and Sewer Pipe Cleaners · 47-4071.00 Compare →
• Maintenance Workers, Machinery · 49-9043.00 Compare →
• Coating, Painting, and Spraying Machine Setters, Operators, and Tenders · 51-9124.00 Compare →
• Machine Feeders and Offbearers · 53-7063.00 Compare →
• Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders · 51-9012.00 Compare →
• Home Appliance Repairers · 49-9031.00 Compare →
• Dishwashers · 35-9021.00 Compare →
• Grinding and Polishing Workers, Hand · 51-9022.00 Compare →
• Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders · 51-9051.00 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 Cleaners of Vehicles and Equipment — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

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.

@misc{singulariki-role-53-7061-00,
  title  = {Cleaners of Vehicles and Equipment},
  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-53-7061-00}
}