# Materials Engineers

> Evaluate materials and develop machinery and processes to manufacture materials for use in products that must meet specialized design and performance specifications. Develop new uses for known materials. Includes those engineers working with composite materials or specializing in one type of material, such as graphite, metal and metal alloys, ceramics and glass, plastics and polymers, and naturally occurring materials. Includes metallurgists and metallurgical engineers, ceramic engineers, and welding engineers.

- **SOC code:** 17-2131.00
- **Canonical URL:** https://singulariki.com/roles/role-17-2131-00
- **Also known as:** Materials Engineer, Materials Research Engineer, Metallurgical Engineer, Metallurgist, Extrusion Engineer, Materials Development Engineer, Research Engineer, Test Engineer
- **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):
- Analyze product failure data and laboratory test results to determine causes of problems and develop solutions.
- Design and direct the testing or control of processing procedures.
- Monitor material performance, and evaluate its deterioration.
- Conduct or supervise tests on raw materials or finished products to ensure their quality.
- Evaluate technical specifications and economic factors relating to process or product design objectives.
- Modify properties of metal alloys, using thermal and mechanical treatments.
- Guide technical staff in developing materials for specific uses in projected products or devices.
- Determine appropriate methods for fabricating and joining materials.
- Review new product plans, and make recommendations for material selection, based on design objectives such as strength, weight, heat resistance, electrical conductivity, and cost.
- Supervise the work of technologists, technicians, and other engineers and scientists.
- Plan and implement laboratory operations to develop material and fabrication procedures that meet cost, product specification, and performance standards.
- Plan and evaluate new projects, consulting with other engineers and corporate executives, as necessary.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Engineering and Technology _(knowledge)_
- Chemistry _(knowledge)_
- Physics _(knowledge)_
- Production and Processing _(knowledge)_
- Mathematics _(knowledge)_
- Reading Comprehension _(essential_skill)_
- Active Listening _(essential_skill)_
- Science _(essential_skill)_
- Complex Problem Solving _(transferable_skill)_
- Oral Comprehension _(ability)_
- Written Comprehension _(ability)_
- Oral Expression _(ability)_

**Skills in demand:**
- Chemistry _(Specialized Skill)_
- Physics _(Specialized Skill)_
- Mathematics _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Complex Problem Solving _(Common Skill)_
- Active Listening _(Common Skill)_
- Critical Thinking _(Common Skill)_
- English Language _(Common Skill)_
- Writing _(Common Skill)_
- Information Ordering _(Specialized Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology, in demand)_
- Microsoft Office software _(hot technology, in demand)_
- Microsoft PowerPoint _(hot technology, in demand)_
- Autodesk AutoCAD _(hot technology)_
- C++ _(hot technology)_
- Dassault Systemes SolidWorks _(hot technology)_
- Microsoft Access _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft SharePoint _(hot technology)_
- Microsoft Visio _(hot technology)_
- Microsoft Visual Basic _(hot technology)_
- Microsoft Word _(hot technology)_

## AI exposure & outlook

- **AI task-overlap index:** 69th percentile (High) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 76th percentile (High) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 69th percentile (High) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 62nd percentile (Moderate) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 15th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 5.7% growth (About average); 1.5k annual openings; 23k → 24.3k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $108,310; 22,770 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

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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-17-2131-00_