Electrical Engineering QA Lead - Remote

About the role

This role involves overseeing quality, consistency, and trainer performance across electrical engineering AI training projects. You will review AI-generated content and work, evaluate output quality, provide feedback, and ensure contributors adhere to quality standards.

Responsibilities

• Spot-check electrical engineering items, identify quality issues, provide ongoing feedback through direct messages (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.