# Shoe Machine Operators and Tenders

> Operate or tend a variety of machines to join, decorate, reinforce, or finish shoes and shoe parts.

- **SOC code:** 51-6042.00
- **Canonical URL:** https://singulariki.com/roles/role-51-6042-00
- **Also known as:** Boot Maker, Inseamer, Side Laster, Stitcher, Cobbler, Insole Department Worker, Shoe Cementer, Shoe Maker
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Inspect finished products to ensure that shoes have been completed according to specifications.
- Align parts to be stitched, following seams, edges, or markings, before positioning them under needles.
- Operate or tend machines to join, decorate, reinforce, or finish shoes and shoe parts.
- Remove and examine shoes, shoe parts, and designs to verify conformance to specifications such as proper embedding of stitches in channels.
- Switch on machines, lower pressure feet or rollers to secure parts, and start machine stitching, using hand, foot, or knee controls.
- Fill shuttle spools with thread from a machine's bobbin winder by pressing a foot treadle.
- Staple sides of shoes, pressing a foot treadle to position and hold each shoe under the feeder of the machine.
- Draw thread through machine guide slots, needles, and presser feet in preparation for stitching, or load rolls of wire through machine axles.
- Study work orders or shoe part tags to obtain information about workloads, specifications, and the types of materials to be used.
- Perform routine equipment maintenance such as cleaning and lubricating machines or replacing broken needles.
- Position dies on material in a manner that will obtain the maximum number of parts from each portion of material.
- Test machinery to ensure proper functioning before beginning production.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Near Vision _(ability)_
- Arm-Hand Steadiness _(ability)_
- Control Precision _(ability)_
- Production and Processing _(knowledge)_
- Reading Comprehension _(essential_skill)_
- Manual Dexterity _(ability)_
- Finger Dexterity _(ability)_
- Administration and Management _(knowledge)_
- Active Listening _(essential_skill)_
- Critical Thinking _(essential_skill)_
- Monitoring _(essential_skill)_
- Operations Monitoring _(transferable_skill)_

**Skills in demand:**
- Reading Comprehension _(Common Skill)_
- Finger Dexterity _(Common Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft PowerPoint _(Common Skill)_
- Microsoft Outlook _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Adobe Acrobat _(Specialized Skill)_
- Active Listening _(Common Skill)_
- Visualization _(Specialized Skill)_
- Speech Recognition _(Specialized Skill)_
- Equipment Maintenance _(Specialized Skill)_

**Tools & technology:**
- Adobe Acrobat _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Word _(hot technology)_
- Inventory tracking software
- Production control software

## AI exposure & outlook

- **AI task-overlap index:** 9th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 17th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 13th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 10th percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 94th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -3.7% growth (Declining); 0.4k annual openings; 4.1k → 3.9k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $38,160; 3,270 employed.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

---
_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-51-6042-00_