Meta's Finance team is undergoing a significant transformation to define and execute our 2030 aspirations. The Modern Finance team is a key enabler of this aspiration, acting as a catalyst for innovation, productivity, and enhanced employee experiences. Within Modern Finance, the Governance & Communication (G&C) group is responsible for deploying critical governance frameworks, driving alignment across Finance, and enabling responsible adoption of transformative technologies — including AI.
As the AI Governance & Automation Manager, you will drive the rollout, operationalization, and continuous optimization of the Finance AI governance program. This role sits at the intersection of controls, technology, and innovation — you will ensure that Finance can harness the full power of AI while maintaining robust compliance with Sarbanes-Oxley (SOX), reliability in our financial reporting, and controls in our core processes. You will drive the deployment of the Meta Finance governance framework that empowers teams to move fast with confidence, rather than creating bureaucratic bottlenecks. While this is an individual contributor role, you will guide and influence leaders across multiple organizations to drive adoption and alignment.
This is a unique opportunity to shape how one of the world's most innovative companies governs AI within its Finance function — balancing rigorous internal controls with the imperative to drive significant autonomy, efficiency, and value through AI adoption. In this role you may also have the opportunity to influence public policy, supporting engagements with key regulatory bodies like the SEC as well as other leading governance bodies like COSO.
Responsibilities
- Program Rollout & Operationalization: Lead the end-to-end rollout of the Finance AI governance framework, translating strategy into actionable processes, policies, and standards that teams can adopt at scale
- SOX & Regulatory Compliance: Ensure AI deployments across Finance maintain full compliance with Sarbanes-Oxley requirements, particularly around IT General Controls and application controls — while designing governance that maximizes autonomy and efficiency rather than constraining innovation
- Framework Optimization: Continuously refine and improve the governance framework based on operational experience, stakeholder feedback, and evolving best practices. This is a rapidly evolving space, and the framework must evolve with it
- System Requirements Definition: Partner with Engineering and Enterprise Products teams to define the technical requirements for the AI governance platform, including workflows, approval processes, risk classification, and monitoring features
- Platform Build & Optimization: Collaborate with engineering teams to build out and continuously enhance the governance system — ensuring it is AI first in how it operates, increasing automation, enabling user self-service, scalability and robust reporting
- Continuous Monitoring Features: Collaborate with core finance functions and EP to develop a robust continuous monitoring framework, starting with our highest risk areas and ultimately looking to establish a broad-based AI powered continuous monitoring technological framework that significantly enhances our control environment
- Technology Integration: Ensure the governance system integrates effectively with Meta's broader technology ecosystem and supports the tools and platforms Finance teams use daily
- Program Health Reporting: Establish and maintain comprehensive reporting for the AI governance program, including health metrics, compliance levels, risk indicators, and adoption dashboards
- Automated Reporting: Design and implement automated reporting features at multiple levels (team, function, leadership), reducing manual effort and increasing timeliness and accuracy of governance insights
- Data-Informed Decision Making: Leverage program data to identify trends, surface risks proactively, and inform continuous improvement of governance policies and processes
- Stakeholder Engagement: Engage with Finance functions and cross-functional teams to ensure they understand AI governance requirements, their responsibilities, and the resources available to them
- Enablement Materials & Training: Develop and deliver (or enable) training programs, toolkits, and reference materials that empower teams to govern AI effectively within their domains. The framework is designed to enable people, not slow them down
- Change Management: Drive adoption through proactive communication, addressing resistance, and demonstrating the value of governance as an enabler of AI-powered transformation
- Technology Monitoring: Maintain a forward-looking perspective on emerging AI technologies used in industry and at Meta, new deployment methodologies, and evolving industry practices that may impact the governance framework
- Regulatory Landscape: Stay current on evolving regulatory requirements, industry standards, and peer company approaches to AI governance to ensure the framework remains leading-edge
- Proactive Framework Updates: Anticipate and proactively address gaps or opportunities in the governance framework informed by new technologies, process changes, or shifts in Meta's AI strategy
- Influence Without Authority: Lead cross-functional initiatives across teams that sit outside of direct reporting lines, including Risk and Controls, Internal Audit and possibly external audit, building consensus and driving alignment through clear communication, trusted relationships, and demonstrated expertise
- Cross Functional Partnership: Collaborate with key partners across Finance (Accounting, Financial Planning & Analysis, Tax, Treasury, Internal Audit), Engineering, Enterprise Product, Legal, and Policy teams to ensure governance approaches are practical, aligned, and effective
- Stakeholder Management: Navigate complex stakeholder landscapes, balancing competing priorities and ensuring all voices are heard while maintaining program momentum
- Collaboration & Relationship Building: Cultivate trusted "One-Team" relationships across Finance functions, Engineering, EP, and other cross-functional partners. Build trust through transparency, reliability, and follow-through
Minimum Qualifications
- 12+ years of experience in Finance, Accounting, and/or Internal Controls
- Demonstrated understanding of Sarbanes-Oxley (SOX) compliance requirements, including IT general controls (ITGCs), application controls, and COSO framework
- Demonstrated experience with program management of enterprise-wide initiatives, including governance, controls, or compliance frameworks
- Proven track record of operating in cross-functional environments and influencing stakeholders without direct authority
- Deep understanding of AI tools and a demonstrated experience showing willingness to continue learning
- Experience navigating ambiguity and building solutions in a rapidly evolving domain
- Bachelor's degree in Accounting, Finance, Business, or a related field 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 reviews)
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
- CPA, CIA, or CISA
- Experience implementing or managing AI/technology governance frameworks within a finance or accounting context
- Working knowledge of AI/ML concepts and hands-on experience with AI tools
- Experience in a large-scale, global technology company
- Accounting & Controls Expertise: Demonstrated understanding of financial controls, SOX compliance, and the intersection of accounting processes with technology — enabling you to serve as a credible advisor to both Finance and Engineering stakeholders
- Program Management: Demonstrated experience managing complex, multi-workstream programs with clear milestones, dependencies, and accountability structures across many different stakeholder groups
- AI Fluency & Innovation state of mind: Experience of learning AI, thinking through tough problems, and building solutions with a variety of tools as well as a track record of adopting and championing new technologies. Willingness to experiment and iterate — not just manage from the sidelines
- Communication & Influence: Evidenced success in communicating and presentation skills, including demonstrated experience organizing complex ideas into clear, compelling narratives. Track record of influencing leaders and driving alignment across organizational boundaries
- Attention to Detail: High standard of precision in governance documentation, reporting, and compliance artifacts while maintaining focus on the bigger picture
- Problem-Solving & Ambiguity Navigation: Experience operating in uncharted territory, developing solutions to novel challenges, and iterating as the landscape evolves
- Leadership: Demonstrated leadership presence with the experience to guide and influence leaders across organizations, drive accountability, and create an environment where people feel empowered to move quickly and responsibly