Electrical Engineering QA Lead - Remote
About the role
This role involves overseeing quality, consistency, and trainer performance across electrical engineering AI training projects. The focus is on reviewing AI-generated content and evaluating output quality against project guidelines.
Responsibilities
Review AI-generated electrical engineering content and trainer/QA work, providing precise written feedback and ensuring adherence to quality standards.
Evaluate work for technical accuracy, engineering reasoning, circuit analysis correctness, calculation validity, standards awareness, unit consistency, safety considerations, clarity, formatting, instruction-following, and adherence to project-specific rubrics.
Assess work for recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently.
Manage quality workflows across remote technical teams, ensuring all contributors meet the required standards.
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.
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.
Qualifications
Bachelor’s or Master’s degree in Electrical Engineering, Electronics Engineering, Computer Engineering, Power Engineering, Telecommunications Engineering, or a closely related engineering field.
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.
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.