Electrical Engineering QA Lead - Remote

About the role

This is an hourly, remote contractor role as an Electrical Engineering Quality Assurance Lead. The role involves overseeing quality, consistency, and trainer performance across electrical engineering AI training projects. Review AI-generated electrical engineering content and trainer/QA work, providing precise written feedback and ensuring all contributors adhere to quality standards.

Responsibilities

• Spot-check electrical engineering items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
• Evaluate AI-generated engineering explanations, circuit analyses, calculations, design recommendations, diagrams/descriptions, troubleshooting steps, and problem-solving workflows for correctness and clarity.
• Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and electrical-engineering-specific review standards.
• Respond to trainer/QA questions clearly and promptly, especially around engineering assumptions, units, formulas, circuits, safety concerns, standards references, and rubric interpretation.
• DM contributors who are inactive or not working, encourage activation, track follow-ups, and flag availability issues when needed.
• Create and maintain electrical engineering project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding materials.
• Schedule and run onboarding/training calls with trainers and QAs to explain project expectations, workflows, rubrics, quality standards, and electrical-engineering-specific review requirements.
• Ensure all trainers and QAs apply engineering guidelines consistently and understand updates as projects evolve.
• Flag unsafe, misleading, or overconfident engineering recommendations, especially where circuits, electrical installations, equipment operation, power systems, high voltage, batteries, or human safety may be affected.
• Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for electrical engineering AI training projects.

Requirements

• Bachelor’s or Master’s degree in Electrical Engineering, Electronics Engineering, Computer Engineering, Power Engineering, Telecommunications Engineering, or a closely related engineering field.
• Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear technical feedback in English.
• 3+ years of professional experience in electrical engineering, electronics, power systems, embedded systems, signal processing, circuit design, controls, telecommunications, technical review, engineering education, or related workflows.
• Strong understanding of core electrical engineering topics such as circuit analysis, analog/digital electronics, electromagnetics, signals and systems, power systems, control systems, semiconductor devices, communication systems, instrumentation, and electrical safety.
• Ability to evaluate engineering content against detailed rubrics and identify issues such as incorrect assumptions, flawed calculations, missing units, unsafe recommendations, invalid circuit logic, hallucinated standards, or incomplete explanations.
• Familiarity with common electrical engineering tools or workflows such as SPICE/LTspice, MATLAB, Simulink, Python, Verilog/VHDL, PCB design tools, oscilloscopes, circuit simulation, embedded workflows, or power-system analysis tools is preferred.
• Experience leading or supporting remote teams of trainers, annotators, reviewers, engineers, technical writers, or QAs is strongly preferred.
• Comfortable working in fast-moving remote environments using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
• Highly detail-oriented and organized, with the ability to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and other quality documentation.
• Experience with AI training, data annotation, large language models, prompt/response evaluation, technical content QA, or rubric-based LLM evaluation is a strong plus.