Head of Trust & Safety

RESPONSIBILITIES: Own the safety of AI output to teachers and students: policy, model-level guardrails, evals, red-teaming, and incident response. Own the trust posture with districts: safety documentation, RFP and school-board responses, and alignment with state AI-in-education guidance. Develop safety evaluations and monitoring as integral components of product infrastructure; ensure that each new tool includes safety acceptance criteria established during the scoping phase, rather than at launch. Own student-facing safety specifically: age-appropriate content, self-harm and crisis handling, and escalation paths to educators. Run the safety incident process end to end and own the postmortem loop back into product and engineering. Partner with Legal on COPPA, FERPA, state privacy laws, and emerging AI regulation. T&S owns the operational reality; Legal owns the legal position. Build and lead a small team (1 to 3 to start), scaling the function through 2027. QUALIFICATIONS / COMPETENCIES / SKILLS. Must Haves: Has owned trust & safety or responsible AI for a consumer or youth-facing product at scale: real incidents, real policy decisions, real regulators. Hands-on with LLM safety: evals, classifiers, prompt/system-level guardrails, red-teaming. Can read an eval result and say what to change. Has made safety a velocity enabler, not friction, by building guardrails as infrastructure so teams ship faster with confidence. Makes judgment calls under ambiguity and owns them publicly, to districts and to the exec team. Strong writer: safety docs, district-facing explainers, incident communications. Operates hypothesis-first and escalates one-way doors fast rather than sitting on them. Nice to Haves: K-12 or youth-platform experience (GoGuardian, Securly, Roblox, Discord, YouTube Kids, Epic Games). Familiarity with state AI-in-education frameworks and district procurement questionnaires. Prior work with child-safety NGOs or standards bodies (Common Sense Media, NCMEC, Thorn, ISTE). Data/ML fluency to partner directly with engineering on classifiers and eval pipelines. Comfortable being the external face of safety at conferences, with press, and in front of district boards. REQUIRED EXPERIENCE: Proven ownership of trust & safety or responsible AI at a consumer or youth-facing product at scale. Hands-on experience with LLM safety tooling: evals, classifiers, and red-teaming. Experience handling live incidents involving minors or vulnerable users. Track record of building safety as product infrastructure, not a gate. Demonstrated ability to communicate safety tradeoffs clearly to executive and district-level audiences. TECH STACK: LLM layer: multi-provider (OpenAI / Anthropic / Google via GCP Vertex); moderation APIs and custom classifiers. Eval and red-team tooling (to be built out); Datadog for output observability and alerting. Product stack context: TypeScript / Next.js on Vercel, Supabase (PostgreSQL) backend. Incident and knowledge tooling: Linear, Slack workflows, Notion.