Job Description
Precise application of circuit theory, signal processing, and power-systems principles is a discipline AI systems need to learn before they can be trusted with electrical-engineering content. Chegg is seeking electrical engineering professionals to evaluate AI-generated technical content, build authoritative reference solutions, and flag flawed engineering reasoning. Fully remote and asynchronous.
Core Responsibilities
- Evaluate AI-generated responses to circuit analysis, signal processing, and power-systems questions for accuracy, reasoning quality, and completeness
- Write reference-standard solutions with correct engineering calculations and circuit reasoning with correct established electrical engineering principles and design standards and clear explanations that AI models learn from
- Identify calculation errors, misapplied circuit laws, or unsafe design assumptions in AI-produced content, tagging the specific error category
- Compare and rank multiple AI responses to the same problem, explaining which best reflects engineering rigor and design soundness
- Test whether AI systems handle ambiguous or safety-critical electrical design scenarios responsibly
- Create realistic synthetic circuit design case studies and troubleshooting scenarios used to train and evaluate models
- Contribute to rubric design — defining scoring criteria such as accuracy, engineering rigor, and clarity — used to benchmark AI performance at scale
- Complete asynchronous task batches independently — no scheduled calls or fixed hours required
Key Qualifications
- Master’s degree or PhD in Electrical Engineering or a closely related field (or equivalent recognized qualification); relevant experience is a plus — postgraduate and doctoral graduates welcome
- Strong grounding in circuit theory, signal processing, or power systems
- Practical or research experience involving circuit design or systems analysis
- Sharp eye for subtle factual, logical, or methodological error — can detect and explain flawed reasoning in plain English
- Self-directed, detail-oriented, and comfortable delivering quality work asynchronously
Nice to Have
- Professional Engineer (PE) license or an equivalent international chartered-engineer credential
- Familiarity with simulation tools (MATLAB, SPICE, Simulink)
- Prior experience in content review, QA, or AI/ML data annotation projects
Why Chegg
- Fully remote and flexible — no fixed schedule
- Task-based commitment, typically 10–40 hours per week
- Real-world challenges with direct impact on AI accuracy and safety
- Pathway to ongoing projects for high-quality contributors