Electrical Engineering QA Lead - Remote
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Key skills for this role
About the Role
This is a remote contract role for an Electrical Engineering Quality Assurance Lead to oversee quality and trainer performance on AI training projects. You will review AI-generated electrical engineering content, provide feedback, and ensure adherence to quality standards.
Key Skills for This Role
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.
- 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.
Full Job Posting
About This Role
- In this hourly, remote contractor role, you will work as an Electrical Engineering Quality Assurance Lead to oversee quality, consistency, and trainer performance across electrical engineering AI training projects.
- You will review AI generated electrical engineering content and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure that all contributors follow the expected quality standards.
- This role requires strong electrical engineering expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote technical teams.
Your Profile
- 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.
Key Responsibilities
- Quality monitoring: Spot check electrical engineering items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
- Technical review: Evaluate AI generated engineering explanations, circuit analyses, calculations, design recommendations, diagrams/descriptions, troubleshooting steps, and problem solving workflows for correctness and clarity.
- Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and electrical engineering specific review standards.
- Question handling: Respond to trainer/QA questions clearly and promptly, especially around engineering assumptions, units, formulas, circuits, safety concerns, standards references, and rubric interpretation.
- Trainer/QA activation management: DM contributors who are inactive or not working, encourage activation, track follow ups, and flag availability issues when needed.
- Documentation: Create and maintain electrical engineering project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding materials.
- Onboarding and training: Schedule and run onboarding/training calls with trainers and QAs to explain project expectations, workflows, rubrics, quality standards, and electrical engineering specific review requirements.
- Quality alignment: Ensure all trainers and QAs apply engineering guidelines consistently and understand updates as projects evolve.
- Risk and safety review: 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.
- Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for electrical engineering AI training projects.
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