Global Response Operations (GRO) is Meta's frontline defense against threats to platform integrity, user safety, regulatory compliance, and advertiser support. The Response teams solve essential escalations, remediate the most critical issues, and respond to urgent risks and incidents. We respond when the stakes are highest: high-priority escalations that demand speed, precision, coordination, and judgement. We work closely with our partners to fix gaps in our platforms. Our mandate is to manage complex and novel situations that require special care across Meta's Family of Apps, all surfaces and domains, all issue types and all user groups. We are transitioning to an agentic operating model where AI handles routine work and human experts focus on the cases, decisions, and system improvements that require judgment, creativity, and domain depth.
You will own Meta's most complex, sensitive, and time-critical escalations, where AI reaches its limits and expert human judgment determines the outcome. Working across all Meta surfaces, products, and issue types, you'll investigate and resolve high-stakes situations spanning civic concerns, entertainment events, advertiser integrity, regulatory risk, and AI product failures.
You'll serve as a domain expert and workflow contributor by designing how humans and AI work together, critically evaluating AI outputs, encoding your expertise into systems that scale, and supporting others to do the same. You'll lead or contribute to pods that drive measurable operational improvement.
This role is for people who navigate ambiguous situations by defining structure, identifying key decisions, and driving toward resolution, prioritize effectively across competing demands, and see AI as a force multiplier for better outcomes.
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
- Disturbing content: This role requires the review of content, issues, and matters that can be offensive or disturbing, including but not limited to graphic violence, self-harm, hate speech, abuse, and sexually explicit material. Meta provides resources and support for employees who work with sensitive content
- On call: This role will require regular scheduled weekend and holiday oncall work, and some non-scheduled crisis intervention
- Pace: The work is interrupt-driven. You'll balance proactive improvement work with reactive escalation response, and the escalations take priority
- AI Forward: This team actively uses AI tools in its daily operations. You're expected to push the boundary of what AI can handle and know where human judgment must intervene
Minimum Qualifications
- 3+ 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
- Strong analytical skills with the ability to synthesize large volumes of qualitative and quantitative information into actionable insights
- 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 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)
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy review)
- Experience with structured AI evaluation (red-teaming, prompt testing, rubric design, failure mode analysis)
- Track record of building AI-powered workflows, agents, or automations that measurably improved team output or efficiency
- Background in one or more of: content integrity, intellectual property, regulatory frameworks, advertiser policies, or civic/political escalations
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Familiarity with Meta's products, policies, and enforcement systems
- Experience leading or mentoring others; ability to drive pod-level accountability for an outcome