Textile cutting machine setters, operators, and tenders
SOC Code: 51-6062
Textile cutting machine setters, operators, and tenders carries a 39% AI exposure score (Medium automation risk), with a median annual wage of $37,940 and -11.7% projected employment growth from 2024 to 2034 (BLS), affecting approximately 9,300 workers. Full task breakdown, skills, and employer data are below.
Proportion of tasks susceptible to AI automation (O*NET analysis)
At 39% AI exposure, Textile cutting machine setters, operators, and tenders sits 2 points above the 37.2% average across 832 U.S. occupations - more exposed than 67% of them. Most of its core tasks still require human judgment.
Career outlook score
16/100
Textile cutting machine setters, operators, and tenders, weighted across 3 of 3 tracked dimensions
A weighted composite of median wage, projected growth, and AI-automation security (inverted exposure), each benchmarked against every other tracked occupation. It describes this occupation's standing across those three dimensions, not a guarantee of future outcomes.
- Median wage F $37,940
Annual median wage, percentile rank vs all tracked occupations
- Projected growth F -11.7%
Projected employment change, 2024-2034, BLS Employment Projections
- AI exposure (inverted) F 39% exposure
Lower O*NET task-automability scores lower risk; this dimension scores security, not exposure
Home health and personal care aides is #1 of 832 occupations by employment (4.3M, AI exposure rank #392 at 35%) vs. Bookkeeping, accounting, and auditing clerks #1 by AI task exposure (95%, employment rank #17 at 1.6M).
Automation-risk inventory
Exclusive cut of 832 occupations by PlainWorkforce automation-risk band. Orthogonal to the continuous AI-exposure distribution chart below and to this occupation's single risk badge, this is the corpus band mix, with Textile cutting machine setters, operators, and tenders in the Medium band.
Credential × growth standing
Open entry on a declining BLS line
Typical entry is HS / some college (333/832) while growth is Declining (111/832). This is the crowded high-risk corner of the map - automation and headcount pressure compound without a degree moat.
Where Textile cutting machine setters, operators, and tenders sits among all occupations
AI-exposure score distribution across 832 U.S. occupations (O*NET task analysis). This occupation is marked in rose.
Where Textile cutting machine setters, operators, and tenders sits by AI exposure
AI-exposure score distribution across U.S. occupations (O*NET task analysis)
39% Around the middle lower than 33% of 832 occupations
Occupations, banded by AI-exposure score
Each bar is a band; taller bars hold more occupations. The dashed line + filled bar mark this entry. Hover or tap any bar for its full count and share, and where it sits relative to this entry.
Source O*NET task data (U.S. Department of Labor) · BLS 2024–2034
Source: O*NET task data (U.S. Department of Labor); AI-exposure scores are PlainWorkforce's analysis. As of BLS 2024–2034.
Textile cutting machine setters, operators, and tenders vs. its occupational neighbors
AI exposure (horizontal) vs projected 2024–2034 growth (vertical) for occupations in the same SOC group. Textile cutting machine setters, operators, and tenders is marked in rose; the top-left corner is the most future-proof.
Source: BLS Employment Projections 2024–2034 (growth) and O*NET (AI-exposure analysis by PlainWorkforce).
Employment Projections
Where This Score Comes From
Textile cutting machine setters, operators, and tenders's Medium automation-risk tier is computed from O*NET Database 30.0 task-level analysis, where each documented task the occupation performs is evaluated against current generative AI, robotic process automation, and machine-learning capabilities. A score below 40% reflects tasks anchored in physical dexterity, unstructured environments, or high-touch human interaction that current AI cannot reliably replicate.
The employment, wage, and education figures above come from a second, separate federal source: BLS Employment Projections 2024–2034, matched to the O*NET task data by Standard Occupational Classification (SOC 51-6062) code, with the wage figure cross-validated against the BLS Occupational Employment and Wage Statistics (OEWS) May 2024 survey. BLS figures are presented exactly as published, with no adjustment on our part; the SOC-code match is what lets Textile cutting machine setters, operators, and tenders be compared directly against every other tracked occupation on the same basis, not just roles that happen to share a job title.
For career planners, this profile should be read alongside the task, skill, and knowledge breakdowns below and the list of employers whose workforce composition includes Textile cutting machine setters, operators, and tenders. Adjacent occupations shown further down offer lateral moves that preserve industry knowledge while potentially reducing exposure.
Education & Entry Requirements
Top Tasks (O*NET)
- 1. Thread yarn, thread, or fabric through guides, needles, and rollers of machines.
- 2. Operate machines to cut multiple layers of fabric into parts for articles such as canvas goods, house furnishings, garments, hats, or stuffed toys.
- 3. Inspect products to ensure that the quality standards and specifications are met.
- 4. Adjust cutting techniques to types of fabrics and styles of garments.
- 5. Place patterns on top of layers of fabric and cut fabric following patterns, using electric or manual knives, cutters, or computer numerically controlled cutting devices.
- 6. Program electronic equipment.
- 7. Study guides, samples, charts, and specification sheets or confer with supervisors or engineering staff to determine set-up requirements.
- 8. Start machines, monitor operations, and make adjustments as needed.
- 9. Stop machines when specified amounts of product have been produced.
- 10. Adjust machine controls, such as heating mechanisms, tensions, or speeds, to produce specified products.
Key Skills Required
- Operations Monitoring
- Operation and Control
- Monitoring
- Quality Control Analysis
- Active Listening
- Speaking
- Coordination
- Equipment Maintenance
- Troubleshooting
- Repairing
Knowledge Areas
- Production and Processing
- Mathematics
- Mechanical
- English Language
- Design
- Education and Training
- Administration and Management
- Computers and Electronics
- Public Safety and Security
- Engineering and Technology
Frequently Asked Questions
Will AI replace Textile cutting machine setters, operators, and tenders?
Textile cutting machine setters, operators, and tenders has an AI exposure score of 39%, indicating a medium level of automation risk. The majority of tasks in this role require human judgment, creativity, or physical presence that AI cannot easily replicate.
What is the job outlook for Textile cutting machine setters, operators, and tenders?
According to BLS Employment Projections 2024-2034, Textile cutting machine setters, operators, and tenders is projected to decline by 11.7% over the decade. Current employment stands at approximately 9,300 workers.
What skills are needed for Textile cutting machine setters, operators, and tenders?
Key skills for Textile cutting machine setters, operators, and tenders include Operations Monitoring, Operation and Control, Monitoring, and others. Typical entry-level education is High school diploma or equivalent.
How much do Textile cutting machine setters, operators, and tenders earn?
The median annual wage for Textile cutting machine setters, operators, and tenders is $37,940, according to BLS Occupational Employment and Wage Statistics (May 2024). Actual earnings vary by location, experience, industry, and employer. The BLS publishes detailed wage percentiles by region in its Occupational Employment and Wage Statistics program.
What education is required for Textile cutting machine setters, operators, and tenders?
The typical entry-level education for Textile cutting machine setters, operators, and tenders is High school diploma or equivalent. Employers generally expect None of related work experience. On-the-job training typically involves Moderate-term on-the-job training. Requirements can vary by employer and specialization.
Which companies employ Textile cutting machine setters, operators, and tenders?
Textile cutting machine setters, operators, and tenders roles exist across many industries and employers. Workforce composition is estimated from BLS industry-occupation employment distributions matched to SEC-registered public companies.
AI Exposure Rating
Medium automation risk based on 10 analyzed tasks. Most tasks require human judgment and are resistant to automation.
Nationwide occupations with similar workforce profiles
Two data-derived peer sets for Textile cutting machine setters, operators, and tenders: AI-exposure neighbors and BLS 2034 growth neighbors (distinct comparison neighborhoods, cross-occupation).
Similar AI exposure score
Nearest O*NET task-automability scores nationwide (39% here).
- Architects, except landscape and naval · AI exposure 39%
- Biological technicians · AI exposure 39%
- Cabinetmakers and bench carpenters · AI exposure 39%
- Career/technical education teachers, middle school · AI exposure 39%
Similar projected growth (2024–2034)
Nearest BLS employment change rates nationwide (-11.7% here).
- Interviewers, except eligibility and loan · growth -11.6%
- Grinding, lapping, polishing, and buffing machine tool setters, operators, and tenders, metal and plastic · growth -12.0%
- Meter readers, utilities · growth -12.0%
- Cutting, punching, and press machine setters, operators, and tenders, metal and plastic · growth -12.1%
Peers are nearest-neighbor matches on published BLS + O*NET metrics among occupations with ≥10,000 workers in 2024; corpus ranks above sort the full tracked set.
Related Occupations
Showing 6 of 81 occupations with a comparable automation-risk profile.
What to do with this
Use Textile cutting machine setters, operators, and tenders' numbers above to compare, not just read in isolation.
- Compare Textile cutting machine setters, operators, and tenders side by side against another occupation on wage, growth, and AI exposure. Workforce comparison tool
- Understand how the AI exposure score above is actually calculated before treating it as a verdict. Reading AI exposure scores
- See the full ranked list of occupations with the lowest automation exposure nationwide. Safest occupations from AI
AI exposure scores estimate task automatability from O*NET data; they are not a certainty of job loss, and BLS growth projections are estimates, not guarantees.
Data sources: Bureau of Labor Statistics Employment Projections 2024–2034 and O*NET Database 30.0. Employment figures are rounded. Wage data from BLS Occupational Employment Statistics (OES).