
Regulatory Escalations Manager
Required skills
- Platform Integrity
- User Safety
- Regulatory Compliance
- Advertiser Support
- Response Operations
- Escalations
- Remediation
- Risk Management
- Incident Response
- High-Priority Escalations
Nice to have
- SLA
- Business Impact
- Reputational Impact
- Multi-system Investigations
- Aligned Response Strategies
- Accountable Next Steps
Role summary
Meta's Global Response Operations (GRO) seeks a Regulatory Escalations Manager to handle the company's most critical and sensitive escalations across all apps and surfaces. This role involves deep investigation, cross-functional coordination with Policy, Legal, and Engineering, and executive-level communication. You will leverage AI tools, design AI-assisted workflows, and translate expert human judgment into scalable logic. Responsibilities include conducting root cause analysis, evaluating AI outputs, and contributing to operational improvements. The role requires managing high-pressure, time-sensitive situations, working on-call, and reviewing potentially disturbing content. A minimum of 4 years in a related operational field is required.
Responsibilities
- Drive Meta's highest-priority escalations to resolution: from initial triage through investigation, cross-functional coordination, and final resolution, ensuring that time-sensitive cases are mitigated within SLA and with minimal business or reputational impact
- Conduct rigorous, multi-system investigations: connect signals across surfaces and stakeholders on complex or novel case types (LLM hallucinations, AI agent misbehavior, deepfake/synthetic media, cross-platform integrity failures), producing root cause analysis and documentation that prevents recurrence
- Coordinate across Policy, Legal, Engineering, Sales, Communications, Leadership, and other external-facing networks: drive aligned response strategies on cases carrying reputational, legal, or business risk, translating complexity into clear decisions and accountable next steps
- Synthesize and deliver executive-level communications: leverage AI to ensure accurate, actionable situational awareness reaches leadership and partners when it matters
- Exercise judgment on unprecedented situations: make high-stakes calls and set a precedent for continuous improvement
- Work on-call shifts within a global team: maintain 24/7 readiness and operate effectively across time zones, delivering consistent quality and decision-making across shifting priorities and time-sensitive situations
- Contribute to workflow performance in your domain: support end-to-end quality across human-in-the-loop and human-on-the-loop deployments, identifying when intervention is needed and flagging issues that affect automated outputs
- Translate expert human judgment into encodable logic: convert tacit domain knowledge (policy interpretation, contextual reasoning, edge case intuition) into explicit rules, prompts, and decision frameworks that AI systems can execute
- Lead or contribute to sprints and pods that transform operations: drive measurable improvements in efficiency, effectiveness, and performance outcomes
- Design and iterate on AI-assisted workflows (prompts, decision trees, runbooks, automations): increase team throughput, measured by reduced handling time, enhanced outcomes, or eliminated manual steps for recurring case patterns
- Support peer development on judgment, AI tool mastery, and escalation calibration: share expertise through case reviews and real-time modeling, contributing to the team's collective capability
- Conduct case quality reviews: identify patterns in errors and AI failures; implement corrections that raise the bar for the team
- Critically evaluate AI-generated assessments, investigation outputs, and recommended actions: correct and document failure patterns, feed structured improvements back into the system so accuracy improves over time
- Identify when regulatory, policy, or operational context changes require capability updates: proactively flag when existing automation rules, prompts, or decision logic have become stale due to external shifts, ensuring that systems reflect the current reality
- Provide structured feedback on AI launches: deliver actionable recommendations for improvement based on operational experience
- Design escalation logic, runbooks, and decision trees for AI systems: define how AI handles edge cases and when it routes to humans
- Create and maintain AI prompts, playbooks, and evaluation rubrics: build the tooling layer that governs AI performance in your domain
- Test and validate new AI capabilities before deployment: ensure the quality and reliability before workflows go live
- Design AI skills and capabilities to check and co-pilot human work: build manual and human-in-the-loop quality mechanisms into operational processes
- Develop and maintain deep domain expertise: stay current on evolving threats, platform dynamics, and policy or legal landscape to inform rapid decision making
- Surface emerging patterns, systemic issues, and root cause hypotheses: translate operational observations into strategic signals that drive action upstream
- Monitor workflow health and flag performance drift: track whether automated systems in your domain continue to produce correct outcomes over time, surfacing degradation signals before errors compound
- Document investigation methodologies and resolution approaches for novel case types: build institutional knowledge that scales beyond individual expertise
- Contribute to demand signal and operational improvement: identify volume drivers, capability gaps, and process inefficiencies that inform roadmap priorities
- Own investigation and resolution of escalations involving Meta's AI products, including LLM outputs, AI agents, and AI-generated content
- Develop expertise in AI system failure modes: build pattern recognition for how and why AI systems produce harmful, inaccurate, or policy-violating outputs
- Partner with cross-functional teams on AI-specific escalation protocols and intervention capabilities: shape how the company detects and responds to AI-driven incidents
- Participate in AI red-teaming and stress testing exercises: build intuition for AI failure cases and contribute findings that strengthen guardrails
Minimum Qualifications
- 4+ years in escalation management, trust & safety, crisis response, investigations, regulatory compliance, policy, or a related operational field
- Demonstrated ability to manage and resolve high-pressure, time-sensitive situations with sound judgment and minimal supervision
- Active, daily proficiency with AI tools — including prompt engineering, AI-assisted investigation, and workflow automation — with the ability to critically evaluate and override AI outputs
- Experience synthesizing large volumes of qualitative and quantitative information into actionable insights (e.g., root cause analyses, trend reports, or executive summaries)
- Clear, structured written communication — able to produce executive-level briefs, case reports, and stakeholder updates (leveraging AI for drafting where appropriate)
- Experience building effective relationships and collaborating across teams with technical and non-technical partners
- Experience identifying process inefficiencies or capability gaps and creating scalable solutions (e.g., new workflows, tools, or runbooks)
- Ability to build alignment and collaborate effectively to move work forward across teams
Preferred Qualifications
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy review)
- Background in one or more of: content integrity, intellectual property, regulatory frameworks, advertiser policies, or civic/political escalations
- Familiarity with Meta's products, policies, and enforcement systems
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Track record of building AI-powered workflows, agents, or automations that measurably improved team output or efficiency
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Experience with structured AI evaluation (red-teaming, prompt testing, rubric design, failure mode analysis)
- Experience leading or mentoring others; ability to drive team-level accountability for an outcome
$100,000/year to $143,000/year + bonus + equity + benefits
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