Design graduate-level computational problems using scikit-rf and ngspice for RF/microwave network analysis, S-parameter characterization, circuit simulation, and frequency response — calibrating tasks against frontier AI models to build advanced AI reasoning benchmarks.
Electrical Engineering Quality Assurance Lead (QAL)
United States
Up to $105/hr
June 15, 2026
Job description
About the Role
This is a remote, hourly contractor role for an Electrical Engineering Quality Assurance Lead (QAL) responsible for overseeing quality, consistency, and contributor performance across electrical engineering AI training projects.
You will review AI-generated electrical engineering content, evaluate trainer and QA work, maintain technical quality standards, support onboarding activities, manage contributor performance, and ensure adherence to engineering review guidelines and evaluation rubrics.
The role combines electrical engineering expertise, technical quality assurance, AI evaluation, safety review, contributor support, and process improvement.
Key Responsibilities
Quality Monitoring
- Review electrical engineering training items and QA outputs
- Identify quality issues and recurring technical errors
- Provide detailed feedback to contributors
- Escalate critical quality concerns
Technical Review
Evaluate AI-generated circuit analyses, electrical calculations, design recommendations, troubleshooting workflows, diagram interpretations, and technical reasoning.
Verify technical accuracy, circuit correctness, calculation validity, unit consistency, engineering assumptions, standards awareness, safety compliance, and clarity of explanation.
Trainer & QA Communication
- Communicate guideline updates and workflow changes
- Clarify electrical-engineering-specific review standards
- Maintain contributor alignment
Question Resolution
Respond to contributor questions regarding circuit behavior, engineering assumptions, electrical calculations, units and conversions, standards references, safety requirements, power systems, design reasoning, and rubric interpretation.
Contributor Activation Management
- Follow up with inactive contributors and track engagement
- Encourage participation and task completion
Documentation Management
Create and maintain style guides, engineering FAQs, quality notes, calibration tasks, examples, trackers, honeypots, and onboarding materials.
Onboarding & Training
- Conduct onboarding and training sessions
- Explain project workflows, evaluation rubrics, engineering quality standards, technical review procedures, and electrical safety requirements
Risk & Safety Review
Identify and flag unsafe recommendations, dangerous electrical advice, high-voltage safety risks, power-system hazards, unsafe installation practices, battery-related risks, and misleading technical conclusions.
Process Improvement
- Identify recurring quality gaps and recommend workflow improvements
- Help build scalable QA processes for electrical engineering projects
Required Skills & Qualifications
- Bachelor's or Master's degree in Electrical Engineering, Electronics Engineering, Computer Engineering, Power Engineering, Telecommunications Engineering, or related discipline
- 3+ years of professional experience in electrical engineering, electronics, power systems, embedded systems, telecommunications, signal processing, circuit design, or control systems
- Strong understanding of circuit analysis, analog and digital electronics, electromagnetics, signals and systems, power systems, control systems, semiconductor devices, communication systems, and electrical safety
- Ability to identify incorrect assumptions, invalid circuit logic, calculation errors, missing units, unsafe recommendations, hallucinated standards, and design flaws
- Strong written English, technical feedback, and documentation skills
Preferred Qualifications
- Experience with LTspice, SPICE, MATLAB, Simulink, Python, Verilog, VHDL, PCB design software, or power-system analysis tools
- Experience leading or supporting engineers, technical reviewers, QA teams, or annotation teams
- Experience with AI training, data annotation, LLM evaluation, or rubric-based QA
- Familiarity with Discord, Google Docs, Google Sheets, and project management systems
Additional Information
- Fully remote — Flexible schedule — Weekly payments
- Access to future opportunities through the SME Careers expert network
About SME Careers
SME Careers is an AI data services company and subsidiary of SuperAnnotate that provides training data for leading AI companies and foundation-model laboratories.
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