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 →Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders
Occupation · SOC 51-9192.00
Operate or tend machines to wash or clean products, such as barrels or kegs, glass items, tin plate, food, pulp, coal, plastic, or rubber, to remove impurities.
Also called: Clean in Places Operator (CIP Operator) · Sanitation Technician · Sanitation Worker · Sanitizer · Anodizer · Filler Operator · Parts Cleaner · Tub Wash Operator · Tub Washer · Wash Crew Person · Acid Dipper · Agitator
Job family: Production Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-51-9192-00/context.md directly.
AI work map
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.
Market signal
Independent published positions, read together — not a forecast.
- About average employment outlook (+3.6% by 2034)
- 1,600 openings/yr
- Low AI exposure
- Median pay $41,460/yr
- Work is mostly on-site (not teleworkable)
- 2013 flagged this for computerization (65th); today's AI task-overlap is comparatively low (9th)
9th-percentile task overlap — yet about 1,600 openings a year (+3.6% projected, BLS) . What exposure means →
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.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) Low | 13th | -1.1 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 15th | 0.1 | |
| AI assistant applicability (Microsoft) Low | 12th | 0.1 |
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.
Historical automation estimate (2013)
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.8 · 65th percentile among occupations · Moderate
How AI is actually used in this job
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Examine and inspect machines to detect malfunctions. | 0.2% |
Job outlook
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | About average · +3.6% by 2034 |
| Projected annual openings | 1,600 |
| Employment 2024 → 2034 | 14,600 → 15,200 |
“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 3 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.
Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders sits at the 41st percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure eased from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
23% mean task exposure (2025)
41st percentile of 427 placed occupations
−2 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Pulp and Papermaking Plant Operators · 8171 | 28% | Minimal |
| Mineral and Stone Processing Plant Operators · 8112 | 21% | Not exposed |
| Metal Finishing, Plating and Coating Machine Operators · 8122 | 20% | Not exposed |
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 11 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Add specified amounts of chemicals to equipment at required times to maintain solution levels and concentrations. Core · importance 4.3
- Observe machine operations, gauges, or thermometers, and adjust controls to maintain specified conditions. Core · importance 4.1
- Set controls to regulate temperature and length of cycles, and start conveyors, pumps, agitators, and machines. Core · importance 4.1
- Draw samples for laboratory analysis, or test solutions for conformance to specifications, such as acidity or specific gravity. Supplemental · importance 4.1
- Adjust, clean, and lubricate mechanical parts of machines, using hand tools and grease guns. Supplemental · importance 4.0
- Drain, clean, and refill machines or tanks at designated intervals, using cleaning solutions or water. Core · importance 4.0
- Operate or tend machines to wash and remove impurities from items such as barrels or kegs, glass products, tin plate surfaces, dried fruit, pulp, animal stock, coal, manufactured articles, plastic, or rubber. Core · importance 4.0
- Record gauge readings, materials used, processing times, or test results in production logs. Core · importance 3.8
- Examine and inspect machines to detect malfunctions. Core · importance 3.8
- Load machines with objects to be processed and unload them after cleaning, placing them on conveyors or racks. Supplemental · importance 3.8
- Measure, weigh, or mix cleaning solutions, using measuring tanks, calibrated rods or suction tubes. Core · importance 3.6
Work activities
- Operate industrial equipment.
- Apply solutions to production equipment.
- Clean production equipment.
- Measure ingredients or substances to be used in production processes.
- Mix substances to create chemical solutions.
- Test chemical or physical characteristics of materials or products.
- Collect samples of materials or products for testing.
- Monitor instruments to ensure proper production conditions.
- Adjust temperature controls of ovens or other heating equipment.
- Operate pumping systems or equipment.
- Inspect production equipment.
- Record operational or production data.
- Lubricate production equipment.
- Load materials into production equipment.
- Remove products or workpieces from production equipment.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Knowledge
| Production and Processing | 3.9 | |
| English Language | 3.0 | |
| Public Safety and Security | 2.8 | |
| Psychology | 2.7 |
Transferable skills
| Operation and Control | 3.3 | |
| Operations Monitoring | 3.1 | |
| Time Management | 2.9 | |
| Social Perceptiveness | 2.8 | |
| Coordination | 2.8 | |
| Judgment and Decision Making | 2.6 |
Abilities
| Near Vision | 3.3 | |
| Information Ordering | 3.1 | |
| Perceptual Speed | 3.1 | |
| Arm-Hand Steadiness | 3.1 | |
| Control Precision | 3.1 | |
| Category Flexibility | 3.0 | |
| Manual Dexterity | 3.0 | |
| Finger Dexterity | 3.0 | |
| Multilimb Coordination | 3.0 | |
| Trunk Strength | 3.0 | |
| Oral Comprehension | 2.9 | |
| Problem Sensitivity | 2.9 | |
| Selective Attention | 2.9 | |
| Extent Flexibility | 2.9 | |
| Auditory Attention | 2.9 | |
| Speech Recognition | 2.9 | |
| Oral Expression | 2.8 | |
| Deductive Reasoning | 2.8 | |
| Inductive Reasoning | 2.8 | |
| Stamina | 2.8 | |
| Far Vision | 2.8 | |
| Speech Clarity | 2.8 | |
| Static Strength | 2.6 | |
| Gross Body Coordination | 2.6 | |
| Depth Perception | 2.6 |
Essential skills
| Active Listening | 2.9 | |
| Speaking | 2.9 | |
| Critical Thinking | 2.9 | |
| Monitoring | 2.9 | |
| Reading Comprehension | 2.8 |
Skills in demand
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Tools & technology
| Example | Category | |
|---|---|---|
| Microsoft Excel | Spreadsheet software | Hot technology |
| Microsoft Word | Word processing software | Hot 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
- High school diploma or equivalent · 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.
| Some College Courses | 3.9% | |
| Associate's Degree (or other 2-year degree) | 3.9% | |
| Post-Secondary Certificate | 0.7% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Career interests (Holland / RIASEC)
| Realistic | 7.0 | |
| Conventional | 4.4 | |
| Investigative | 1.7 |
Interest areas
| Physical/Manual Labor | 4.0 | |
| Mechanics/Electronics | 2.0 | |
| Transportation/Machine Operation | 1.7 | |
| Engineering | 1.6 | |
| Physical Science | 1.2 | |
| Culinary Art | 1.2 | |
| Accounting | 1.2 | |
| Agriculture | 1.1 | |
| Management/Administration | 1.1 | |
| Health Care Service | 1.1 |
Work styles
| Dependability | 2.2 | |
| Attention to Detail | 1.9 | |
| Cautiousness | 1.7 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $32,770 |
| 25th percentile | $37,140 |
| Median (50th) | $41,460 |
| 75th percentile | $48,280 |
| 90th percentile | $56,590 |
| People employed | 13,890 |
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.
| Industry | Workers | National median pay |
|---|---|---|
| Manufacturing · Sector | 10,970 | $42,120 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 900 | $36,200 |
| Wholesale Trade · Sector | 750 | $42,600 |
| Temporary Help Services · National industry | 720 | $35,000 |
| Mining, Quarrying, and Oil and Gas Extraction · Sector | 320 | $51,420 |
| Agriculture, Forestry, Fishing and Hunting · Sector | 300 | $38,460 |
| Transportation and Warehousing · Sector | 250 | $47,130 |
| Machine Shops · National industry | 240 | $39,350 |
| Other Services (except Public Administration) · Sector | 140 | $37,090 |
| Professional, Scientific, and Technical Services · Sector | 70 | $43,380 |
| Construction · Sector | — | $47,400 |
| Retail Trade · Sector | — | $30,590 |
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).
| Industry | Concentration | Workers |
|---|---|---|
| Machine Shops · National industry | 10.26× | 240 |
| Manufacturing · Sector | 9.54× | 10,970 |
| Agriculture, Forestry, Fishing and Hunting · Sector | 7.87× | 300 |
| Mining, Quarrying, and Oil and Gas Extraction · Sector | 6.19× | 320 |
| Temporary Help Services · National industry | 3.02× | 720 |
| Wholesale Trade · Sector | 1.38× | 750 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 1.11× | 900 |
| Transportation and Warehousing · Sector | 0.38× | 250 |
Related occupations
Part of the Advanced Manufacturing career cluster.
- Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders · 51-9012.00 Compare →
- Mixing and Blending Machine Setters, Operators, and Tenders · 51-9023.00 Compare →
- Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders · 51-9051.00 Compare →
- Textile Bleaching and Dyeing Machine Operators and Tenders · 51-6061.00 Compare →
- Laundry and Dry-Cleaning Workers · 51-6011.00 Compare →
- Cleaners of Vehicles and Equipment · 53-7061.00 Compare →
- Cooling and Freezing Equipment Operators and Tenders · 51-9193.00 Compare →
- Chemical Plant and System Operators · 51-8091.00 Compare →
- Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders · 51-9021.00 Compare →
- Heat Treating Equipment Setters, Operators, and Tenders, Metal and Plastic · 51-4191.00 Compare →
- Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders · 51-9041.00 Compare →
- Chemical Equipment Operators and Tenders · 51-9011.00 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 Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Skills that travel
Capabilities this work builds that are used across many other occupations.
Adjacent roles
Occupations O*NET rates as related — the nearby moves on the map.
- Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders Compare →
- Mixing and Blending Machine Setters, Operators, and Tenders Compare →
- Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders Compare →
- Textile Bleaching and Dyeing Machine Operators and Tenders Compare →
- Laundry and Dry-Cleaning Workers Compare →
Paths in
How people typically prepare for this work.
- High school diploma or equivalent · typical entry level
- Job zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed · preparation tier
Zoom out
On the global GenAI exposure gradient this work sits around the 41st percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders show 9th-percentile AI task overlap — and about 1,600 annual U.S. openings
- Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders rank in the 9th 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 1,600 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 about average (+3.6%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $41,460, across about 13,890 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders show 9th-percentile AI task overlap — and about 1,600 annual U.S. openings • Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders rank in the 9th 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 1,600 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 about average (+3.6%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $41,460, across about 13,890 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders". https://singulariki.com/roles/role-51-9192-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
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.
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
- Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
- 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.
Cite this page
Plain
Singulariki. "Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders." 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; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); 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. https://singulariki.com/roles/role-51-9192-00
APA
Singulariki. (2026). Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-51-9192-00
BibTeX
@misc{singulariki-role-51-9192-00,
title = {Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); 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-51-9192-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.