Skills it runs on
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Mixing and Blending Machine Setters, Operators, and Tenders
Occupation · SOC 51-9023.00
Set up, operate, or tend machines to mix or blend materials, such as chemicals, tobacco, liquids, color pigments, or explosive ingredients.
Also called: Blender · Machine Operator · Mixer · Mixer Operator · Batchmaker · Blending Technician (Blending Tech) · Ink Blender · Issuing Operator · Operator · Stock Preparation Operator (Stock Prep Operator) · Abrasive Mixer · Acetylene Cylinder Packing Mixer
Job family: Production Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-51-9023-00/context.md directly.
AI work map
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Market signal
Independent published positions, read together — not a forecast.
- Declining employment outlook (-6.8% by 2034)
- 8,800 openings/yr
- Low AI exposure
- Median pay $47,680/yr
- Work is mostly on-site (not teleworkable)
- 2013 flagged this for computerization (68th); today's AI task-overlap is comparatively low (18th)
18th-percentile task overlap — yet about 8,800 openings a year (-6.8% projected, BLS) . What exposure means →
AI & job outlook
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Exposure to current AI
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) Low | 25th | -0.8 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 15th | 0.1 | |
| AI assistant applicability (Microsoft) Low | 22nd | 0.1 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.1), and including AI-powered software (γ 0.1). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.
Mixed signals. Today's AI/LLM studies show relatively low exposure for this job, but the older (2013) Frey–Osborne work rated it higher for computerization and robotics. Different eras, different technologies — the AI measures above reflect the current state.
Historical automation estimate (2013)
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.8 · 68th percentile among occupations · High
How AI is actually used in this job
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Record operational or production data on specified forms. | 0.2% |
Job outlook
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Declining · -6.8% by 2034 |
| Projected annual openings | 8,800 |
| Employment 2024 → 2034 | 101,100 → 94,300 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
Where this work sits on the global GenAI gradient
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international 2 occupations below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
Mixing and Blending Machine Setters, Operators, and Tenders sits at the 32nd percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure rose from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
20% mean task exposure (2025)
32nd percentile of 427 placed occupations
+2 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Cement, Stone and Other Mineral Products Machine Operators · 8114 | 23% | Not exposed |
| Glass and Ceramics Plant Operators · 8181 | 17% | Not exposed |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
Tasks
All 19 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Weigh or measure materials, ingredients, or products to ensure conformance to requirements. Core · importance 4.6
- Read work orders to determine production specifications or information. Core · importance 4.6
- Observe production or monitor equipment to ensure safe and efficient operation. Core · importance 4.6
- Mix or blend ingredients by starting machines and mixing for specified times. Core · importance 4.5
- Stop mixing or blending machines when specified product qualities are obtained and open valves and start pumps to transfer mixtures. Core · importance 4.5
- Compound or process ingredients or dyes, according to formulas. Core · importance 4.5
- Examine materials, ingredients, or products visually or with hands to ensure conformance to established standards. Core · importance 4.5
- Dump or pour specified amounts of materials into machinery or equipment. Core · importance 4.4
- 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. Core · importance 4.4
- Record operational or production data on specified forms. Core · importance 4.4
- Collect samples of materials or products for laboratory testing. Core · importance 4.4
- Unload mixtures into containers or onto conveyors for further processing. Core · importance 4.3
- Test samples of materials or products to ensure compliance with specifications, using test equipment. Core · importance 4.3
- Add or mix chemicals or ingredients for processing, using hand tools or other devices. Core · importance 4.2
- Tend accessory equipment, such as pumps or conveyors, to move materials or ingredients through production processes. Core · importance 4.2
- Transfer materials, supplies, or products between work areas, using moving equipment or hand tools. Core · importance 4.2
- Clean and maintain equipment, using hand tools. Core · importance 4.1
- Open valves to drain slurry from mixers into storage tanks. Supplemental · importance 4.0
- Dislodge and clear jammed materials or other items from machinery or equipment, using hand tools. Supplemental · importance 3.9
Work activities
- Measure ingredients or substances to be used in production processes.
- Weigh finished products.
- Measure dimensions of completed products or workpieces to verify conformance to specifications.
- Test chemical or physical characteristics of materials or products.
- Operate cooking, baking, or other food preparation equipment.
- Operate mixing equipment.
- Load materials into production equipment.
- Monitor equipment operation to ensure proper functioning.
- Operate pumping systems or equipment.
- Collect samples of materials or products for testing.
- Mix substances to create chemical solutions.
- Record operational or production data.
- Move products, materials, or equipment between work areas.
- Read work orders or other instructions to determine product specifications or materials requirements.
- Clean production equipment.
- Maintain production or processing equipment.
- Clear equipment jams.
- Adjust equipment controls to regulate flow of production materials or products.
- Clean work areas.
- Clean facilities or work areas.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Knowledge
| Production and Processing | 4.3 | |
| English Language | 3.0 |
Transferable skills
| Operations Monitoring | 3.6 | |
| Operation and Control | 3.6 | |
| Equipment Maintenance | 3.1 | |
| Troubleshooting | 3.1 | |
| Repairing | 3.1 | |
| Quality Control Analysis | 3.1 | |
| Time Management | 3.1 | |
| Coordination | 3.0 | |
| Judgment and Decision Making | 3.0 |
Abilities
| Near Vision | 3.4 | |
| Arm-Hand Steadiness | 3.3 | |
| Manual Dexterity | 3.3 | |
| Oral Comprehension | 3.1 | |
| Written Comprehension | 3.1 | |
| Problem Sensitivity | 3.1 | |
| Information Ordering | 3.1 | |
| Category Flexibility | 3.1 | |
| Perceptual Speed | 3.1 | |
| Selective Attention | 3.1 | |
| Control Precision | 3.1 | |
| Multilimb Coordination | 3.1 | |
| Reaction Time | 3.1 | |
| Far Vision | 3.1 | |
| Oral Expression | 3.0 | |
| Deductive Reasoning | 3.0 | |
| Inductive Reasoning | 3.0 | |
| Finger Dexterity | 3.0 | |
| Response Orientation | 3.0 | |
| Rate Control | 3.0 | |
| Static Strength | 3.0 | |
| Trunk Strength | 3.0 | |
| Visual Color Discrimination | 3.0 | |
| Speech Clarity | 3.0 |
Essential skills
| Reading Comprehension | 3.1 | |
| Critical Thinking | 3.1 | |
| Monitoring | 3.1 | |
| Speaking | 3.0 | |
| Active Listening | 2.9 |
Skills in demand
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Tools & technology
| Example | Category | |
|---|---|---|
| Microsoft Excel | Spreadsheet software | Hot technology |
| Microsoft Office software | Office suite software | Hot technology |
| Microsoft Outlook | Electronic mail software | Hot technology |
| Microsoft Windows | Operating system software | Hot technology |
| Microsoft Word | Word processing software | Hot technology |
| SAP software | Enterprise resource planning ERP software | Hot technology |
| Email software | Electronic mail software | |
| Operational databases | Data base user interface and query software |
Work context
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
How to get in
- Job zone
- Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
- Education
- Usually requires a high school diploma or GED, though some occupations may not.
- Typical entry-level education
- High school diploma or equivalent · BLS, the typical path — not a requirement
- Related experience
- Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
- Preparation level
- SVP (Below 6.0) — total schooling plus on-the-job experience.
Education of current workers
Share of people in this occupation at each level of education.
| High School Diploma | 80.3% | |
| Post-Secondary Certificate | 11.9% | |
| Some College Courses | 6.3% | |
| Less than a High School Diploma | 1.5% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Career interests (Holland / RIASEC)
| Realistic | 6.6 | |
| Conventional | 4.2 | |
| Investigative | 1.9 |
Interest areas
| Physical/Manual Labor | 4.4 | |
| Transportation/Machine Operation | 2.3 | |
| Mechanics/Electronics | 2.3 | |
| Engineering | 2.1 | |
| Culinary Art | 1.7 | |
| Physical Science | 1.7 | |
| Mathematics/Statistics | 1.4 | |
| Agriculture | 1.3 | |
| Medical Science | 1.3 |
Work styles
| Dependability | 3.0 | |
| Attention to Detail | 2.5 | |
| Cautiousness | 2.4 | |
| Integrity | 1.3 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $35,000 |
| 25th percentile | $39,800 |
| Median (50th) | $47,680 |
| 75th percentile | $57,940 |
| 90th percentile | $67,570 |
| People employed | 100,840 |
Industries that employ this occupation
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Manufacturing · Sector | 82,210 | $48,100 |
| Wholesale Trade · Sector | 8,620 | $47,540 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 4,570 | $37,310 |
| Temporary Help Services · National industry | 3,180 | $36,810 |
| Construction · Sector | 1,270 | $48,030 |
| Mining, Quarrying, and Oil and Gas Extraction · Sector | 1,180 | $51,260 |
| Retail Trade · Sector | 1,080 | $39,010 |
| Professional, Scientific, and Technical Services · Sector | 580 | $56,370 |
| Management of Companies and Enterprises · Sector | 470 | $49,840 |
| Transportation and Warehousing · Sector | 380 | $47,200 |
| Agriculture, Forestry, Fishing and Hunting · Sector | 330 | $39,100 |
| Pharmacies and Drug Retailers · National industry | 200 | $40,860 |
Where this work is most concentrated
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Manufacturing · Sector | 9.85× | 82,210 |
| Mining, Quarrying, and Oil and Gas Extraction · Sector | 3.15× | 1,180 |
| Wholesale Trade · Sector | 2.18× | 8,620 |
| Temporary Help Services · National industry | 1.83× | 3,180 |
| Agriculture, Forestry, Fishing and Hunting · Sector | 1.19× | 330 |
| Poured Concrete Foundation and Structure Contractors · National industry | 0.83× | 140 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 0.77× | 4,570 |
| Pharmacies and Drug Retailers · National industry | 0.43× | 200 |
Related occupations
Part of the Advanced Manufacturing career cluster.
- Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders · 51-9012.00 Compare →
- Food Batchmakers · 51-3092.00 Compare →
- Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders · 51-9192.00 Compare →
- Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders · 51-9021.00 Compare →
- Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders · 51-9041.00 Compare →
- Machine Feeders and Offbearers · 53-7063.00 Compare →
- Adhesive Bonding Machine Operators and Tenders · 51-9191.00 Compare →
- Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders · 51-9051.00 Compare →
- Textile Bleaching and Dyeing Machine Operators and Tenders · 51-6061.00 Compare →
- Chemical Equipment Operators and Tenders · 51-9011.00 Compare →
- Packaging and Filling Machine Operators and Tenders · 51-9111.00 Compare →
- Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic · 51-4033.00 Compare →
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
What you can do with this
Options the data surfaces for Mixing and Blending Machine Setters, Operators, and Tenders — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Skills that travel
Capabilities this work builds that are used across many other occupations.
Adjacent roles
Occupations O*NET rates as related — the nearby moves on the map.
- Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders Compare →
- Food Batchmakers Compare →
- Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders Compare →
- Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders Compare →
- Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders Compare →
Paths in
How people typically prepare for this work.
- High school diploma or equivalent · typical entry level
- Job zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed · preparation tier
Zoom out
On the global GenAI exposure gradient this work sits around the 32nd percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Mixing and Blending Machine Setters, Operators, and Tenders show 18th-percentile AI task overlap — and about 8,800 annual U.S. openings
- Mixing and Blending Machine Setters, Operators, and Tenders rank in the 18th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
- The occupation is projected to see about 8,800 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
- BLS projects employment to be declining (-6.8%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $47,680, across about 100,840 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Mixing and Blending Machine Setters, Operators, and Tenders show 18th-percentile AI task overlap — and about 8,800 annual U.S. openings • Mixing and Blending Machine Setters, Operators, and Tenders rank in the 18th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 8,800 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be declining (-6.8%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $47,680, across about 100,840 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Mixing and Blending Machine Setters, Operators, and Tenders". https://singulariki.com/roles/role-51-9023-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Sources for this page
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
- O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
- BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
- BLS Employment Projections 2024–2034 U.S. Bureau of Labor Statistics
- Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
- Microsoft “Working with AI” working-with-ai Microsoft Research
- “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
- AI Occupational Exposure (AIOE) Felten, Raj & Seamans academic
- ILO / Gmyrek et al. GenAI exposure gradient 2025 International Labour Organization
- IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022 Institute for Structural Research (IBS)
- Frey & Osborne (2013) frey-osborne-automation academic
- Dingel & Neiman (2020) dingel-neiman-workathome academic
Data compiled June 2, 2026. Figures are estimates, not advice.
Cite this page
Plain
Singulariki. "Mixing and Blending Machine Setters, Operators, and Tenders." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-51-9023-00
APA
Singulariki. (2026). Mixing and Blending Machine Setters, Operators, and Tenders. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-51-9023-00
BibTeX
@misc{singulariki-role-51-9023-00,
title = {Mixing and Blending Machine Setters, Operators, and Tenders},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-51-9023-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.