# Cutting and Slicing Machine Setters, Operators, and Tenders

> Set up, operate, or tend machines that cut or slice materials, such as glass, stone, cork, rubber, tobacco, food, paper, or insulating material.

- **SOC code:** 51-9032.00
- **Canonical URL:** https://singulariki.com/roles/role-51-9032-00
- **Also known as:** Cutter, Cutter Operator, Machine Operator, Paper Cutter, Cutting Pressman, Die Cutter Operator, Flat Cutter, Sheeter
- **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):
- Set up, operate, or tend machines that cut or slice materials, such as glass, stone, cork, rubber, tobacco, food, paper, or insulating material.
- Examine, measure, and weigh materials or products to verify conformance to specifications, using measuring devices, such as rulers, micrometers, or scales.
- Review work orders, blueprints, specifications, or job samples to determine components, settings, and adjustments for cutting and slicing machines.
- Press buttons, pull levers, or depress pedals to start and operate cutting and slicing machines.
- Start machines to verify setups, and make any necessary adjustments.
- Feed stock into cutting machines, onto conveyors, or under cutting blades, by threading, guiding, pushing, or turning handwheels.
- Mark cutting lines or identifying information on stock, using marking pencils, rulers, or scribes.
- Stack and sort cut material for packaging, further processing, or shipping, according to types and sizes of material.
- Monitor operation of cutting or slicing machines to detect malfunctions or to determine whether supplies need replenishment.
- Adjust machine controls to alter position, alignment, speed, or pressure.
- Remove completed materials or products from cutting or slicing machines, and stack or store them for additional processing.
- Remove defective or substandard materials from machines, and readjust machine components so that products meet standards.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Finger Dexterity _(ability)_
- Control Precision _(ability)_
- Near Vision _(ability)_
- Operations Monitoring _(transferable_skill)_
- Arm-Hand Steadiness _(ability)_
- Operation and Control _(transferable_skill)_
- Problem Sensitivity _(ability)_
- Manual Dexterity _(ability)_
- Quality Control Analysis _(transferable_skill)_
- Production and Processing _(knowledge)_
- Oral Comprehension _(ability)_
- Visualization _(ability)_

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

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

## AI exposure & outlook

- **AI task-overlap index:** 15th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 15th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 13th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 25th percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 72nd percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -2.3% growth (Declining); 5.3k annual openings; 49k → 47.9k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $45,700; 47,540 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-51-9032-00_