Note: The job is a remote job and is open to candidates in USA. YO IT Consulting is a fast-growing AI Data Services company delivering training data for many of the world’s largest AI companies and foundation-model labs. They are seeking an Electrical Engineering Quality Assurance Lead to oversee quality and consistency across electrical engineering AI training projects, ensuring that engineering training data is accurate and aligned with client expectations.

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

Skills

• 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
• 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
• 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
• Experience leading or supporting remote teams of trainers, annotators, reviewers, engineers, technical writers, or QAs
• Experience with AI training, data annotation, large language models, prompt/response evaluation, technical content QA, or rubric-based LLM evaluation

Company Overview