# Manufacturing Engineers

> Design, integrate, or improve manufacturing systems or related processes. May work with commercial or industrial designers to refine product designs to increase producibility and decrease costs.

- **SOC code:** 17-2112.03
- **Canonical URL:** https://singulariki.com/roles/role-17-2112-03
- **Also known as:** Manufacturing Engineer, Plant Engineer, Process Engineer, Process Improvement Engineer, Facility Engineer, Advance Manufacturing Engineer, Automation Engineer, Design 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):
- Troubleshoot new or existing product problems involving designs, materials, or processes.
- Investigate or resolve operational problems, such as material use variances or bottlenecks.
- Identify opportunities or implement changes to improve manufacturing processes or products or to reduce costs, using knowledge of fabrication processes, tooling and production equipment, assembly methods, quality control standards, or product design, materials and parts.
- Apply continuous improvement methods, such as lean manufacturing, to enhance manufacturing quality, reliability, or cost-effectiveness.
- Provide technical expertise or support related to manufacturing.
- Incorporate new manufacturing methods or processes to improve existing operations.
- Review product designs for manufacturability or completeness.
- Determine root causes of failures or recommend changes in designs, tolerances, or processing methods, using statistical procedures.
- Prepare reports summarizing information or trends related to manufacturing performance.
- Design layout of equipment or workspaces to achieve maximum efficiency.
- Prepare documentation for new manufacturing processes or engineering procedures.
- Communicate manufacturing capabilities, production schedules, or other information to facilitate production processes.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Production and Processing _(knowledge)_
- Engineering and Technology _(knowledge)_
- Mechanical _(knowledge)_
- Reading Comprehension _(essential_skill)_
- Mathematics _(essential_skill)_
- Complex Problem Solving _(transferable_skill)_
- Design _(knowledge)_
- Oral Comprehension _(ability)_
- Problem Sensitivity _(ability)_
- Deductive Reasoning _(ability)_
- Inductive Reasoning _(ability)_
- Category Flexibility _(ability)_

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

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

## AI exposure & outlook

- **AI task-overlap index:** 85th percentile (High) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 80th percentile (High) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 81st percentile (High) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 81st percentile (High) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 18th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** yes — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 11.0% growth (Growing fast); 25.2k annual openings; 351.1k → 389.6k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $101,140; 350,230 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-17-2112-03_