Headline risk
18%
Moderate RiskTextile bleaching and dyeing machine operators and tenders
United States AI Work Index tracks this occupation on the shared structural baseline and then layers on local demand resilience, wages, and confidence.
Why This Score
Share of job tasks that overlap with current AI capabilities
Median annual wage
Projected employment change over 10 years
Typical preparation needed for this occupation
Occupation profile
Operate or tend machines to bleach, shrink, wash, dye, or finish textiles or synthetic or glass fibers.
Task evidence
95% weighted task match · 3% effective coverage
Scores combine AI task overlap, human advantages, and local demand. How it works
United States Now
Median Wage
USD 37,320
Employment 2024
6.2K
Projected Change (2024–34)
-10.1%
Openings (2024–34)
0.7K
Wage distribution
Demand outlook
Projections published, but no prose outlook available.
Role Profile
Tasks
- 1. Notify supervisors or mechanics of equipment malfunctions. AI use: 0%
- 2. Adjust equipment controls to maintain specified heat, tension, and speed. AI use: 0%
- 3. Start and control machines and equipment to wash, bleach, dye, or otherwise process and finish fabric, yarn, thread, or other textile goods. AI use: 0%
- 4. Monitor factors such as temperatures and dye flow rates to ensure that they are within specified ranges. AI use: 0%
- 5. Examine and feel products to identify defects and variations from coloring and other processing standards. AI use: 0%
- 6. Confer with coworkers to get information about order details, processing plans, or problems that occur. AI use: 0%
Technologies
Requirements
Work context
Worker profile
Median age 40.4 · 242K employed
Under 25: 16% · 25–54: 64% · 55+: 21%
Related
No direct US role match is available yet for this occupation.
Source coverage
11/11 source families · O*NET 30.2 / OEWS 2024 / ORS 2025 / OOH 2025-08-28 / Projections 2024-34 / CPS 2025 / Anthropic task penetration
Mapping quality
title_match · employment series present
Narrative & sources
Published limitations
This page shows the local country layer, not realised individual job outcomes. The global structural baseline is shared across countries; only the local demand and wage layer changes here.
Built from O*NET occupation descriptions, task statements, technology skills, work context, Job Zones, Anthropic task penetration, BLS OEWS wages, BLS projection tables, BLS ORS requirements, BLS OOH narrative content, BLS skills data, and BLS CPS occupation age tables.