# Layout Workers, Metal and Plastic

> Lay out reference points and dimensions on metal or plastic stock or workpieces, such as sheets, plates, tubes, structural shapes, castings, or machine parts, for further processing. Includes shipfitters.

- **SOC code:** 51-4192.00
- **Canonical URL:** https://singulariki.com/roles/role-51-4192-00
- **Also known as:** Layout Inspector, Layout Man, Layout Technician (Layout Tech), Layout Worker, Development Mechanic, Layout Fabricator, Layout Fitter, Layout Mechanic
- **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):
- Mark curves, lines, holes, dimensions, and welding symbols onto workpieces, using scribes, soapstones, punches, and hand drills.
- Plan locations and sequences of cutting, drilling, bending, rolling, punching, and welding operations, using compasses, protractors, dividers, and rules.
- Fit and align fabricated parts to be welded or assembled.
- Locate center lines and verify template positions, using measuring instruments such as gauge blocks, height gauges, and dial indicators.
- Plan and develop layouts from blueprints and templates, applying knowledge of trigonometry, design, effects of heat, and properties of metals.
- Lay out and fabricate metal structural parts such as plates, bulkheads, and frames.
- Install doors, hatches, brackets, and clips.
- Compute layout dimensions, and determine and mark reference points on metal stock or workpieces for further processing, such as welding and assembly.
- Brace parts in position within hulls or ships for riveting or welding.
- Lift and position workpieces in relation to surface plates, manually or with hoists, and using parallel blocks and angle plates.
- Inspect machined parts to verify conformance to specifications.
- Add dimensional details to blueprints or drawings made by other workers.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Mathematics _(knowledge)_
- Near Vision _(ability)_
- Design _(knowledge)_
- Visualization _(ability)_
- Arm-Hand Steadiness _(ability)_
- Mechanical _(knowledge)_
- Problem Sensitivity _(ability)_
- Manual Dexterity _(ability)_
- Information Ordering _(ability)_
- Production and Processing _(knowledge)_
- Engineering and Technology _(knowledge)_
- English Language _(knowledge)_

**Skills in demand:**
- Mathematics _(Common Skill)_
- Visualization _(Specialized Skill)_
- Information Ordering _(Specialized Skill)_
- English Language _(Common Skill)_
- Finger Dexterity _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft PowerPoint _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Critical Thinking _(Common Skill)_

**Tools & technology:**
- Autodesk AutoCAD _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Word _(hot technology)_
- Hexagon Metrology PC-DMIS
- Inventory tracking software
- Optical Gaging Products Measure-X

## AI exposure & outlook

- **AI task-overlap index:** 22nd percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 28th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 29th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 19th percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 69th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -5.4% growth (Declining); 0.5k annual openings; 5.7k → 5.4k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $61,870; 5,610 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/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **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

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-51-4192-00_