# Mixing and Blending Machine Setters, Operators, and Tenders

> Set up, operate, or tend machines to mix or blend materials, such as chemicals, tobacco, liquids, color pigments, or explosive ingredients.

- **SOC code:** 51-9023.00
- **Canonical URL:** https://singulariki.com/roles/role-51-9023-00
- **Also known as:** Blender, Machine Operator, Mixer, Mixer Operator, Batchmaker, Blending Technician (Blending Tech), Ink Blender, Issuing 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):
- Weigh or measure materials, ingredients, or products to ensure conformance to requirements.
- Read work orders to determine production specifications or information.
- Observe production or monitor equipment to ensure safe and efficient operation.
- Mix or blend ingredients by starting machines and mixing for specified times.
- Stop mixing or blending machines when specified product qualities are obtained and open valves and start pumps to transfer mixtures.
- Examine materials, ingredients, or products visually or with hands to ensure conformance to established standards.
- Compound or process ingredients or dyes, according to formulas.
- Operate or tend machines to mix or blend any of a wide variety of materials, such as spices, dough batter, tobacco, fruit juices, chemicals, livestock feed, food products, color pigments, or explosive ingredients.
- Dump or pour specified amounts of materials into machinery or equipment.
- Record operational or production data on specified forms.
- Collect samples of materials or products for laboratory testing.
- Unload mixtures into containers or onto conveyors for further processing.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Production and Processing _(knowledge)_
- Operations Monitoring _(transferable_skill)_
- Operation and Control _(transferable_skill)_
- Near Vision _(ability)_
- Arm-Hand Steadiness _(ability)_
- Manual Dexterity _(ability)_
- Reading Comprehension _(essential_skill)_
- Critical Thinking _(essential_skill)_
- Monitoring _(essential_skill)_
- Equipment Maintenance _(transferable_skill)_
- Troubleshooting _(transferable_skill)_
- Repairing _(transferable_skill)_

**Skills in demand:**
- Time Management _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Equipment Maintenance _(Specialized Skill)_
- Critical Thinking _(Common Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft Windows _(Common Skill)_
- Microsoft Outlook _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Finger Dexterity _(Common Skill)_
- Deductive Reasoning _(Common Skill)_

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

## AI exposure & outlook

- **AI task-overlap index:** 18th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 25th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 15th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 22nd percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 68th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -6.8% growth (Declining); 8.8k annual openings; 101.1k → 94.3k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $47,680; 100,840 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-51-9023-00_