The AI Safety Engineer track covers alignment techniques, RLHF and constitutional AI, red-teaming, model evaluation for safety properties, and responsible AI deployment. Sessions probe your reasoning on safety-reliability trade-offs.
The AI Safety Engineer track covers alignment fundamentals, RLHF and constitutional AI, red-teaming methodologies, safety evaluation frameworks, jailbreak and adversarial prompt detection, content moderation architectures, and behavioral questions on safety-product trade-offs.
If you describe a red-teaming process, Alex follows up on how you scope the threat model, what categories of harm you test for, and how you prioritize findings. Follow-ups probe the depth and rigor of your safety thinking.
AI Safety Engineer is a dedicated track with its own question bank focused on safety-specific domains. It is distinct from the AI Engineer track, which focuses on LLM integration and applied AI product development.
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