# Chemical Engineers

> Design chemical plant equipment and devise processes for manufacturing chemicals and products, such as gasoline, synthetic rubber, plastics, detergents, cement, paper, and pulp, by applying principles and technology of chemistry, physics, and engineering.

- **SOC code:** 17-2041.00
- **Canonical URL:** https://singulariki.com/roles/role-17-2041-00
- **Also known as:** Chemical Engineer, Development Engineer, Engineer, Process Engineer, Engineering Scientist, Process Control Engineer, Project Engineer, Refinery Process 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):
- Develop safety procedures to be employed by workers operating equipment or working in close proximity to ongoing chemical reactions.
- Troubleshoot problems with chemical manufacturing processes.
- Monitor and analyze data from processes and experiments.
- Evaluate chemical equipment and processes to identify ways to optimize performance or to ensure compliance with safety and environmental regulations.
- Design and plan layout of equipment.
- Prepare estimate of production costs and production progress reports for management.
- Perform tests and monitor performance of processes throughout stages of production to determine degree of control over variables such as temperature, density, specific gravity, and pressure.
- Conduct research to develop new and improved chemical manufacturing processes.
- Determine most effective arrangement of operations such as mixing, crushing, heat transfer, distillation, and drying.
- Develop processes to separate components of liquids or gases or generate electrical currents, using controlled chemical processes.
- Perform laboratory studies of steps in manufacture of new products and test proposed processes in small-scale operation, such as a pilot plant.
- Design measurement and control systems for chemical plants based on data collected in laboratory experiments and in pilot plant operations.

**Emerging tasks** (O*NET):
- Adapt processes to convert from small-scale laboratory operations to large-scale commercial production.
- Develop process flow diagrams or pipe and instrumentation diagrams.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Engineering and Technology _(knowledge)_
- Chemistry _(knowledge)_
- Mathematics _(knowledge)_
- Science _(essential_skill)_
- Critical Thinking _(essential_skill)_
- Reading Comprehension _(essential_skill)_
- Complex Problem Solving _(transferable_skill)_
- Design _(knowledge)_
- Oral Comprehension _(ability)_
- Written Comprehension _(ability)_
- Oral Expression _(ability)_
- Problem Sensitivity _(ability)_

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

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

## AI exposure & outlook

- **AI task-overlap index:** 75th percentile (High) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 91st percentile (High) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 71st percentile (High) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 59th percentile (Moderate) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 13th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 2.6% growth (About average); 1.1k annual openings; 21.6k → 22.1k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $121,860; 20,330 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-17-2041-00_