Drimva

TikTok

Global Head of Long-Term Containment - Trust & Safety

San Jose, California · Director · Full-time · Posted Oct 28, 2026

Experience
5+ years in Trust & Safety, Risk, or adjacent integrity domains; 5+ years managing managers and leading larger organisations across multiple regions.

About the job

Leads TikTok’s global Long-Term Containment function, setting enforcement strategy and managing leaders who deliver durable mitigations for persistent, high-severity harms.

What you'll do

  • Set global containment strategy and allocate enforcement capacity across prioritized harm areas; own outcomes such as prevalence, discoverability, escalation reduction, and recidivism.
  • Lead and develop regional leaders, establish operating models and performance standards, and oversee roadmap, OKRs, and headcount planning.
  • Partner with Prevention and cross-functional teams to turn escalation signals into systemic mitigations, and represent the function to leadership and external safety organizations.

What they ask for

  • 5+ years of experience in Trust & Safety, Risk, or adjacent integrity domains.
  • 5+ years of experience managing managers and leading larger organisations across multiple regions.
  • Demonstrated ownership of enforcement or risk strategy across several harm areas at scale, with a record of shipping systemic, long-term mitigations.
  • Exceptional communication and stakeholder-management skills, including influencing senior leaders and representing a function to external partners.

Nice to have

  • Strong data fluency and experience partnering with Data Science, Product and Engineering teams on metrics, measurement, and tooling.
  • Experience scaling Trust & Safety, enforcement, or investigations organizations; direct experience in two or more harm domains; or embedding AI and LLM tooling into Trust & Safety workflows.

Tools

AI, LLM

AI focus

Preferred experience embedding AI and LLM tooling into Trust & Safety workflows to improve analyst throughput, enforcement precision, or detection of adversarial content.

Model-written summary · the original posting is authoritative

First seen Oct 28, 2026 · Last verified Nov 6, 2026 (UTC)