# Recycling and Reclamation Workers

> Prepare and sort materials or products for recycling. Identify and remove hazardous substances. Dismantle components of products such as appliances.

- **SOC code:** 53-7062.04
- **Canonical URL:** https://singulariki.com/roles/role-53-7062-04
- **Also known as:** Non-Ferrous Material Handler, Sort Line Worker, Sorter, Transfer Station Operator, Bobcat Driver, Box Sorter, Convenience Recycle Center Technician (Convenience Recycle Center Tech), Deconstruction and Decontamination Waste Operations Specialist (D and D Waste Operations Specialist)
- **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):
- Collect and sort recyclable construction materials, such as concrete, drywall, plastics, or wood, into containers.
- Sort materials, such as metals, glass, wood, paper or plastics, into appropriate containers for recycling.
- Extract chemicals from discarded appliances, such as air conditioners or refrigerators, using specialized machinery, such as refrigerant recovery equipment.
- Deposit recoverable materials into chutes or place materials on conveyor belts.
- Clean recycling yard by sweeping, raking, picking up broken glass and loose paper debris, or moving barrels and bins.
- Operate balers to compress recyclable materials into bundles or bales.
- Clean materials, such as metals, according to recycling requirements.
- Operate forklifts, pallet jacks, power lifts, or front-end loaders to load bales, bundles, or other heavy items onto trucks for shipping to smelters or other recycled materials processing facilities.
- Sort metals to separate high-grade metals, such as copper, brass, and aluminum, for recycling.
- Record logs of recycled materials or waste chemicals removed from products.
- Operate processing equipment, such as fiber-sorters and grinders, to sort, crush, or grind recyclable materials.
- Clean, inspect, or lubricate recyclable collection equipment or perform routine maintenance or minor repairs on recycling equipment, such as star gears, finger sorters, destoners, belts, and grinders.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Production and Processing _(knowledge)_
- Manual Dexterity _(ability)_
- Control Precision _(ability)_
- Mechanical _(knowledge)_
- Arm-Hand Steadiness _(ability)_
- Multilimb Coordination _(ability)_
- Near Vision _(ability)_
- Administration and Management _(knowledge)_
- Public Safety and Security _(knowledge)_
- Education and Training _(knowledge)_
- Operation and Control _(transferable_skill)_
- Category Flexibility _(ability)_

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

**Tools & technology:**
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Word _(hot technology)_
- Work scheduling software

## AI exposure & outlook

- **AI task-overlap index:** 4th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 3rd percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 7th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 12th percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 71st percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 1.5% growth (About average); 384.3k annual openings; 2,988.9k → 3,033.1k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $38,940; 2,982,530 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/
- **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-53-7062-04_