# Validation Engineers

> Design or plan protocols for equipment or processes to produce products meeting internal and external purity, safety, and quality requirements.

- **SOC code:** 17-2112.02
- **Canonical URL:** https://singulariki.com/roles/role-17-2112-02
- **Also known as:** Quality Assurance Engineer, Quality Engineer, Quality Management Systems Engineer, Supplier Quality Engineer, Corporate Quality Engineer, Product Quality Engineer, Reliability Engineer, Validation Specialist
- **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):
- Study product characteristics or customer requirements to determine validation objectives and standards.
- Analyze validation test data to determine whether systems or processes have met validation criteria or to identify root causes of production problems.
- Develop validation master plans, process flow diagrams, test cases, or standard operating procedures.
- Prepare detailed reports or design statements, based on results of validation and qualification tests or reviews of procedures and protocols.
- Maintain validation test equipment.
- Conduct validation or qualification tests of new or existing processes, equipment, or software in accordance with internal protocols or external standards.
- Communicate with regulatory agencies regarding compliance documentation or validation results.
- Prepare, maintain, or review validation and compliance documentation, such as engineering change notices, schematics, or protocols.
- Recommend resolution of identified deviations from established product or process standards.
- Design validation study features, such as sampling, testing, or analytical methodologies.
- Prepare validation or performance qualification protocols for new or modified manufacturing processes, systems, or equipment for production of pharmaceuticals, electronics, or other products.
- Create, populate, or maintain databases for tracking validation activities, test results, or validated systems.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Engineering and Technology _(knowledge)_
- Production and Processing _(knowledge)_
- Reading Comprehension _(essential_skill)_
- Written Comprehension _(ability)_
- Written Expression _(ability)_
- Deductive Reasoning _(ability)_
- Writing _(essential_skill)_
- Speaking _(essential_skill)_
- Critical Thinking _(essential_skill)_
- Oral Comprehension _(ability)_
- Oral Expression _(ability)_
- Problem Sensitivity _(ability)_

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

**Tools & technology:**
- Amazon Web Services AWS software _(hot technology, in demand)_
- Docker _(hot technology, in demand)_
- IBM Terraform _(hot technology, in demand)_
- JavaScript _(hot technology, in demand)_
- Kubernetes _(hot technology, in demand)_
- Microsoft Azure software _(hot technology, in demand)_
- Microsoft Excel _(hot technology, in demand)_
- Microsoft Office software _(hot technology, in demand)_
- Microsoft Power BI _(hot technology, in demand)_
- Microsoft Power Platform software _(hot technology, in demand)_
- Microsoft PowerPoint _(hot technology, in demand)_
- Oracle Java _(hot technology, in demand)_

## AI exposure & outlook

- **AI task-overlap index:** 83rd 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):** 79th 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-17-2112-02_