Set up, operate, or tend machines that knit, loop, weave, or draw in textiles.
U.S. Workers
14,530
Median Salary
$38,260
10-Year Growth
-11.2%
Annual Openings
1,700
Typical entry: High school diploma or equivalent
20 of 20 tasks have some AI capability
Exposure Trend
This score reflects estimated AI technical capability for tasks in this occupation. It does not predict employment changes, and it does not account for company-specific constraints, regulation, or adoption barriers.
Inspect products to ensure that specifications are met and to determine if machines need adjustment.
AI: Fully automatable - Computer vision and sensor systems can inspect textile products and infer when machines need adjustment enabling fully automated inspection and feedback in many plants.
Observe woven cloth to detect weaving defects.
AI: Fully automatable - Machine vision models are effective at detecting weaving defects in real time and are widely deployed in automated weaving inspection systems.
Examine looms to determine causes of loom stoppage, such as warp filling, harness breaks, or mechanical defects.
AI: Fully automatable - AI systems using camera, vibration, and process-data analytics can and do diagnose common causes of loom stoppages (warp/fill breaks, harness issues, mechanical faults) with high reliability in industrial settings by 2025.
Notify supervisors or repair staff of mechanical malfunctions.
AI: Fully automatable - Automatically detecting malfunctions and notifying supervisors or repair staff is a straightforward automation task already implemented via monitoring and alerting systems.
Inspect machinery to determine whether repairs are needed.
AI: Fully automatable - Inspection for repair needs can be largely automated by 2025 using sensor fusion, predictive maintenance models, and visual inspection algorithms to detect wear and faults.
Start machines, monitor operations, and make adjustments as needed.
AI: Fully automatable - Starting, monitoring, and making routine operational adjustments are readily automated using control systems and AI-driven process control loops in modern textile plants.
Record information about work completed and machine settings.
AI: Fully automatable - Recording work logs and machine settings is fully automatable through digital sensors, MES/SCADA integration, and automated reporting systems.
Program electronic equipment.
AI: Fully automatable - By 2025 AI tools can generate, debug, and configure software/firmware and configuration for electronic equipment end-to-end, often requiring only limited human oversight for testing and deployment.
Study guides, loom patterns, samples, charts, or specification sheets, or confer with supervisors or engineering staff to determine setup requirements.
AI: Fully automatable - AI can read and synthesize guides, patterns, specs, and charts and produce setup requirements or recommended configurations and can simulate or reason about them in conversations with staff.
Stop machines when specified amounts of product have been produced.
AI: Fully automatable - Stopping machines based on production counts is a simple control task that can be fully automated with counters and process control logic.
Adjust machine heating mechanisms, tensions, and speeds to produce specified products.
AI: Fully automatable - Adjusting heating, tension, and speed is well within AI-driven closed-loop process control and adaptive optimization systems that can reliably produce specified product characteristics.
Remove defects in cloth by cutting and pulling out filling.
AI: Partial - Localized defect repair like cutting and pulling out filling requires fine tactile manipulation and visual judgment that is only partially automatable with specialized equipment.
Thread yarn, thread, and fabric through guides, needles, and rollers of machines for weaving, knitting, or other processing.
AI: Partial - Threading yarn and fabric requires fine, variable manual dexterity and frequent re-threading in unconstrained contexts, so 2025 robotic/AI systems can automate some controlled setups but not fully replace skilled human operators.
Set up, or set up and operate textile machines that perform textile processing and manufacturing operations such as winding, twisting, knitting, weaving, bonding, or stretching.
AI: Partial - Setting up textile machines involves bespoke mechanical adjustments and material handling that can be partially automated for repeatable jobs but still often requires human intervention for complex or variable setups.
Clean, oil, and lubricate machines, using air hoses, cleaning solutions, rags, oil cans, or grease guns.
AI: Partial - Cleaning, oiling, and lubricating involve varied, dexterous manual tasks and hazardous contexts that some robotic systems can handle in limited cases, but full automation across diverse machines is not yet widespread.
Operate machines for test runs to verify adjustments and to obtain product samples.
AI: Partial - AI systems can run test cycles, control machine parameters, and analyze sensor/output data, but physically obtaining and handling product samples and making nuanced tactile judgments still typically requires humans or specialized hardware.
Wash and blend wool, yarn, or cloth.
AI: Partial - Washing and blending at scale is amenable to automated control and recipe optimization by AI, but the physical handling, quality-sensing of fibrous materials, and ad-hoc adjustments still often need human operators or dedicated machinery.
Confer with co-workers to obtain information about orders, processes, or problems.
AI: Partial - AI can facilitate and partially handle information exchange (alerts, summaries, knowledge retrieval), but nuanced, real-time collaborative problem-solving and social negotiation among coworkers still commonly require humans.
Repair or replace worn or defective needles and other components, using hand tools.
AI: Partial - AI can diagnose faults and provide step-by-step guidance for replacing needles and parts, but reliably performing fine manual repairs with hand tools in unstructured settings remains largely a human task without specialized robotics.
Install, level, and align machine components such as gears, chains, guides, dies, cutters, or needles to set up machinery for operation.
AI: Partial - AI can plan and direct installation and alignment and control actuatorized tooling, but precise physical installation, leveling, and manual adjustments still usually require skilled human technicians or advanced robotic systems not yet widespread.