AI Evaluation
Electrical Engineer Quality Assurance Lead (QAL)
Status: Open
- Pay
- $105/hr
- Location
- Remote — The United States
- Employment type
- Contractor
About this opportunity
In this hourly, remote contractor role, you will work as an Electrical Engineering Quality Assurance Lead (QAL) 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.
You will assess 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. You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently. 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.
This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your electrical engineering quality leadership will directly help improve the world’s premier AI models by ensuring that engineering training data is accurate, logically sound, clearly explained, well-documented, and aligned with client expectations.
Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.
Required skills
- Trainer Feedback
- Technical Review
- Electronics
- Power Systems
- Circuit Analysis
- Documentation
- Electrical Engineering
- Engineering QA
- AI Training
- LLM evaluation
What you'll do
- 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.
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.