# Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders

> 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.

- **SOC code:** 51-9192.00
- **Canonical URL:** https://singulariki.com/roles/role-51-9192-00
- **Also known as:** Clean in Places Operator (CIP Operator), Sanitation Technician, Sanitation Worker, Sanitizer, Anodizer, Filler Operator, Parts Cleaner, Tub Wash Operator
- **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):
- Add specified amounts of chemicals to equipment at required times to maintain solution levels and concentrations.
- Observe machine operations, gauges, or thermometers, and adjust controls to maintain specified conditions.
- Set controls to regulate temperature and length of cycles, and start conveyors, pumps, agitators, and machines.
- Draw samples for laboratory analysis, or test solutions for conformance to specifications, such as acidity or specific gravity.
- Adjust, clean, and lubricate mechanical parts of machines, using hand tools and grease guns.
- Drain, clean, and refill machines or tanks at designated intervals, using cleaning solutions or water.
- 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.
- Record gauge readings, materials used, processing times, or test results in production logs.
- Examine and inspect machines to detect malfunctions.
- Load machines with objects to be processed and unload them after cleaning, placing them on conveyors or racks.
- Measure, weigh, or mix cleaning solutions, using measuring tanks, calibrated rods or suction tubes.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Production and Processing _(knowledge)_
- Operation and Control _(transferable_skill)_
- Near Vision _(ability)_
- Operations Monitoring _(transferable_skill)_
- Information Ordering _(ability)_
- Perceptual Speed _(ability)_
- Arm-Hand Steadiness _(ability)_
- Control Precision _(ability)_
- English Language _(knowledge)_
- Category Flexibility _(ability)_
- Manual Dexterity _(ability)_
- Finger Dexterity _(ability)_

**Skills in demand:**
- Information Ordering _(Specialized Skill)_
- English Language _(Common Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Finger Dexterity _(Common Skill)_
- Time Management _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Critical Thinking _(Common Skill)_
- Active Listening _(Common Skill)_
- Social Perceptiveness _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Inductive Reasoning _(Common Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology)_
- Microsoft Word _(hot technology)_

## AI exposure & outlook

- **AI task-overlap index:** 9th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 13th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 15th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 12th percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 65th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 3.6% growth (About average); 1.6k annual openings; 14.6k → 15.2k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $41,460; 13,890 employed.

## 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-51-9192-00_