Machine Feeders and Offbearers

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.

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.

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

“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 occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.

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 13 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

• Inspect materials and products for defects, and to ensure conformance to specifications. Core · importance 4.4
• Record production and operational data, such as amount of materials processed. Core · importance 4.4
• Push dual control buttons and move controls to start, stop, or adjust machinery and equipment. Core · importance 4.3
• Weigh or measure materials or products to ensure conformance to specifications. Core · importance 4.3
• Identify and mark materials, products, and samples, following instructions. Core · importance 4.2
• Clean and maintain machinery, equipment, and work areas to ensure proper functioning and safe working conditions. Core · importance 4.1
• Load materials and products into machines and equipment, or onto conveyors, using hand tools and moving devices. Core · importance 4.0
• Remove materials and products from machines and equipment, and place them in boxes, trucks or conveyors, using hand tools and moving devices. Supplemental · importance 4.0
• Transfer materials and products to and from machinery and equipment, using industrial trucks or hand trucks. Core · importance 4.0
• Shovel or scoop materials into containers, machines, or equipment for processing, storage, or transport. Supplemental · importance 3.9
• Open and close gates of belt and pneumatic conveyors on machines that are fed directly from preceding machines. Supplemental · importance 3.9
• Fasten, package, or stack materials and products, using hand tools and fastening equipment. Core · importance 3.9
• Add chemicals, solutions, or ingredients to machines or equipment as required by the manufacturing process. Supplemental · importance 3.6

Work activities

• Inspect work to ensure standards are met.
• Inspect items for damage or defects.
• Package materials or products.
• Operate conveyors or other industrial material moving equipment.
• Mark materials or objects for identification.
• Load materials into equipment for processing.
• Clean facilities or work areas.
• Clean machinery or equipment.
• Measure product or material dimensions.
• Weigh materials to ensure compliance with specifications.
• Record operational or production data.
• Move materials, equipment, or supplies.
• Shovel materials.

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Abilities

Knowledge

Transferable skills

Essential skills

Skills in demand

Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.

• Mathematics Common Skill
• Microsoft Outlook Common Skill
• Microsoft Excel Common Skill
• Information Ordering Specialized Skill
• English Language Common Skill
• Speech Recognition Specialized Skill
• Finger Dexterity Common Skill
• Reading Comprehension Common Skill
• Active Listening Common Skill
• Inductive Reasoning Common Skill
• Deductive Reasoning Common Skill
• Visualization Specialized Skill
• Time Management Common Skill
• Social Perceptiveness Common Skill
• Depth Perception Common Skill
• Critical Thinking Common Skill
• Writing Common Skill
• Learning Strategies Specialized Skill
• Instructing Specialized Skill
• Complex Problem Solving Common Skill
• Active Learning Common Skill
• Negotiation Common Skill
• Equipment Maintenance Specialized Skill
• Psychology Specialized Skill
• Chemistry Specialized Skill
• Telecommunications Specialized Skill
• Systems Analysis Specialized Skill
• Equipment Selection Specialized Skill
• Physics Specialized Skill
• Geography Specialized Skill
• Foreign Language Common Skill
• Biology Specialized Skill
• Installation Specialized Skill
• Industrial Control Software Specialized Skill

Tools & technology

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

No formal educational credential · 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.

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Career interests (Holland / RIASEC)

Interest areas

Work styles

Wages & employment

U.S. · annual wages (BLS OEWS)

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.

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).

Related occupations

Part of the Advanced Manufacturing career cluster.

• Cutting and Slicing Machine Setters, Operators, and Tenders · 51-9032.00 Compare →
• Adhesive Bonding Machine Operators and Tenders · 51-9191.00 Compare →
• Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders · 51-9041.00 Compare →
• Woodworking Machine Setters, Operators, and Tenders, Except Sawing · 51-7042.00 Compare →
• Paper Goods Machine Setters, Operators, and Tenders · 51-9196.00 Compare →
• Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic · 51-4033.00 Compare →
• Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders · 51-9021.00 Compare →
• Packers and Packagers, Hand · 53-7064.00 Compare →
• Rolling Machine Setters, Operators, and Tenders, Metal and Plastic · 51-4023.00 Compare →
• Laborers and Freight, Stock, and Material Movers, Hand · 53-7062.00 Compare →
• Grinding and Polishing Workers, Hand · 51-9022.00 Compare →
• Packaging and Filling Machine Operators and Tenders · 51-9111.00 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 Machine Feeders and Offbearers — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

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
• 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.

@misc{singulariki-role-53-7063-00,
  title  = {Machine Feeders and Offbearers},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; 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-53-7063-00}
}