# Packaging and Filling Machine Operators and Tenders

> Operate or tend machines to prepare industrial or consumer products for storage or shipment. Includes cannery workers who pack food products.

- **SOC code:** 51-9111.00
- **Canonical URL:** https://singulariki.com/roles/role-51-9111-00
- **Also known as:** Bundler, Filler Operator, Machine Operator, Packaging Operator, Closing Machine Operator, Computer Numerical Control Machine Operator (CNC Machine Operator), Packing Attendant, Packing Machine 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):
- Attach identification labels to finished packaged items, or cut stencils and stencil information on containers, such as lot numbers or shipping destinations.
- Sort, grade, weigh, and inspect products, verifying and adjusting product weight or measurement to meet specifications.
- Stop or reset machines when malfunctions occur, clear machine jams, and report malfunctions to a supervisor.
- Observe machine operations to ensure quality and conformity of filled or packaged products to standards.
- Remove finished packaged items from machine and separate rejected items.
- Monitor the production line, watching for problems such as pile-ups, jams, or glue that isn't sticking properly.
- Inspect and remove defective products and packaging material.
- Tend or operate machine that packages product.
- Start machine by engaging controls.
- Clean, oil, and make minor adjustments or repairs to machinery and equipment, such as opening valves or setting guides.
- Regulate machine flow, speed, or temperature.
- Adjust machine components and machine tension and pressure according to size or processing angle of product.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Production and Processing _(knowledge)_
- Mechanical _(knowledge)_
- Operations Monitoring _(transferable_skill)_
- Arm-Hand Steadiness _(ability)_
- Public Safety and Security _(knowledge)_
- Education and Training _(knowledge)_
- English Language _(knowledge)_
- Monitoring _(essential_skill)_
- Oral Comprehension _(ability)_
- Oral Expression _(ability)_
- Problem Sensitivity _(ability)_
- Perceptual Speed _(ability)_

**Skills in demand:**
- English Language _(Common Skill)_
- Mathematics _(Common Skill)_
- Visualization _(Specialized Skill)_
- Speech Recognition _(Specialized Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft PowerPoint _(Common Skill)_
- Microsoft Outlook _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Finger Dexterity _(Common Skill)_
- Active Listening _(Common Skill)_
- Time Management _(Common Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Word _(hot technology)_
- SAP software _(hot technology)_
- Email software
- Label printing 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.):** 18th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 3rd percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 3rd percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 97th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 4.5% growth (About average); 45.3k annual openings; 381.2k → 398.2k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $40,900; 383,860 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-51-9111-00_