Job Description
Writing clean, correct code and reasoning through algorithmic tradeoffs is a discipline AI models are still learning. Chegg is seeking computer science professionals to evaluate AI-generated code, build authoritative reference solutions, and stress-test model behavior on programming and systems-design tasks. Fully remote and asynchronous.
Core Responsibilities
- Evaluate AI-generated responses to coding, algorithms, and systems-design questions for accuracy, reasoning quality, and completeness
- Write reference-standard code solutions with correct complexity analysis with correct best practices in software design and testing and clear explanations that AI models learn from
- Identify bugs, incorrect complexity claims, security flaws, or flawed logic in AI-produced content, tagging the specific error category
- Compare and rank multiple AI responses to the same problem, explaining which best reflects correctness, readability, and performance
- Test whether AI systems handle edge cases, ambiguous requirements, and adversarial or exploit-seeking prompts safely and correctly
- Create realistic synthetic coding problems and debugging scenarios used to train and evaluate models
- Contribute to rubric design — defining scoring criteria such as correctness, efficiency, readability, and test coverage — 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 Computer Science, Software Engineering, or a related field (or equivalent recognized qualification); relevant experience is a plus — postgraduate and doctoral graduates welcome
- Strong proficiency in at least one major language (Python, Java, or C++) and solid grounding in data structures and algorithms
- Practical experience with version control (Git), code review, and automated testing frameworks
- 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
- Experience with distributed systems at scale — Kubernetes orchestration, Kafka-based event streaming, or distributed tracing (Jaeger, OpenTelemetry)
- Background in machine learning systems, competitive programming, or technical interviewing
- Prior experience in code 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