# Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders

> Set up, operate, or tend machines, such as glass-forming machines, plodder machines, and tuber machines, to shape and form products such as glassware, food, rubber, soap, brick, tile, clay, wax, tobacco, or cosmetics.

- **SOC code:** 51-9041.00
- **Canonical URL:** https://singulariki.com/roles/role-51-9041-00
- **Also known as:** Extruder Operator, Extrusion Operator, Machine Operator, Press Operator, Glass Forming Crew Member, Tuber Operator, Abrasive Wheel Molder, Air Bag Curer
- **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):
- Adjust machine components to regulate speeds, pressures, and temperatures, and amounts, dimensions, and flow of materials or ingredients.
- Press control buttons to activate machinery and equipment.
- Examine, measure, and weigh materials or products to verify conformance to standards, using measuring devices such as templates, micrometers, or scales.
- Activate machines to shape or form products, such as candy bars, light bulbs, balloons, or insulation panels.
- Monitor machine operations and observe lights and gauges to detect malfunctions.
- Notify supervisors when extruded filaments fail to meet standards.
- Clear jams, and remove defective or substandard materials or products.
- Record and maintain production data, such as meter readings, and quantities, types, and dimensions of materials produced.
- Select and install machine components, such as dies, molds, and cutters, according to specifications, using hand tools and measuring devices.
- Review work orders, specifications, or instructions to determine materials, ingredients, procedures, components, settings, and adjustments for extruding, forming, pressing, or compacting machines.
- Turn controls to adjust machine functions, such as regulating air pressure, creating vacuums, and adjusting coolant flow.
- Clean dies, arbors, compression chambers, and molds, using swabs, sponges, or air hoses.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Operations Monitoring _(transferable_skill)_
- Operation and Control _(transferable_skill)_
- Perceptual Speed _(ability)_
- Rate Control _(ability)_
- Reaction Time _(ability)_
- Production and Processing _(knowledge)_
- Arm-Hand Steadiness _(ability)_
- Mechanical _(knowledge)_
- Monitoring _(essential_skill)_
- Problem Sensitivity _(ability)_
- Manual Dexterity _(ability)_
- Near Vision _(ability)_

**Skills in demand:**
- Information Ordering _(Specialized Skill)_
- Finger Dexterity _(Common Skill)_
- Visualization _(Specialized Skill)_
- Deductive Reasoning _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Reading Comprehension _(Common Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft Outlook _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Depth Perception _(Common Skill)_
- Critical Thinking _(Common Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft Word _(hot technology)_
- SAP software _(hot technology)_
- Operational databases
- Production scheduling software

## AI exposure & outlook

- **AI task-overlap index:** 13th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 16th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 10th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 21st percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 84th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 2.0% growth (About average); 5.2k annual openings; 57.3k → 58.4k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $45,130; 57,310 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-51-9041-00_