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Meta

Technical Program Manager, Risk Organization

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What they do

A Risk Manager evaluates risk and recommends and manages strategies to mitigate or offset risks that could result in financial losses for a company or organization. Analyzes current risks and assesses potential new risks. Reviews contracts and insurance policies; prepares risk budgets. Communicates risk policies and procedures to staff at all levels of a company.

$104,706 / year median in Washington

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Job Description

Meta's Risk organization seeks a Technical Program Manager (TPM) to lead complex, large-scale programs that strengthen how risk is managed across the company. In this key position, you will collaborate across engineering, product, legal, policy, and compliance teams to design, build, and scale the frameworks, controls, and tooling that keep Meta's products and infrastructure trustworthy at scale. You will be responsible for driving risk programs end to end, from identifying and assessing emerging risks through the design, implementation, and sustained operation of controls and safeguards. This includes developing and refining repeatable frameworks for risk assessment, mitigation, and evidence of effectiveness, ensuring robust and predictable execution, and proactively resolving technical and organizational challenges to maintain program momentum. You will use your problem-solving, technical acumen, and business insight to scale how risk is identified, mitigated, and demonstrably managed across Meta's products and infrastructure. You will communicate transparently across all levels, motivate multidisciplinary teams, and champion best practices to deliver durable, auditable outcomes that reduce risk and strengthen trust in Meta's systems.
Qualifications:
BS in EE, CS, ME, or related technical field, or equivalent experience 12+ years in software engineering, hardware engineering, systems engineering, or technical product/program management Knowledge of software and hardware development for large-scale hardware readiness, including end-to-end product development processes Excel at clearly communicating complex technical investments simply and understandably Experience delivering complex technology programs and products from inception through successful delivery Knowledge of understanding user needs, gathering requirements, defining project scope Experience working under own initiative across multiple teams, with critical thinking and thought leadership in ambiguous spaces Experience defining and optimizing engineering processes at scale Experience building cross-functional relationships and navigating complex challenges Experience analyzing and solving complex technical problems in large-scale systems (root cause analysis, capacity planning, system design trade-offs, risk assessment) Experience building relationships across multi-disciplinary teams and partners in different time zones Experience defining strategic direction and identifying new opportunities for impact across products, platforms, programs Experience communicating at the executive level and influencing leadership and technical management teams Knowledge of Large Language Models, machine learning, and scaling distributed systems Knowledge of privacy, security, or regulatory compliance domains and how they apply to large-scale systems (keep the original instead if this role covers AI risk) Demonstrated experience in identifying new opportunities for the larger organization and influencing stakeholders Proven commitment to scaling risk management and infrastructure for large-scale AI distributed compute systems Ongoing AI skill development (prompt/context engineering, agent orchestration) and staying current with emerging AI tech Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Adhering to and implementing responsible, ethical AI practices (risk assessment, bias mitigation, quality/accuracy reviews) Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Integrating AI tools to optimize/redesign workflows with measurable impact Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Communicating complex technical investments clearly to executive and cross-functional stakeholders Knowledge of regulatory or audit environments and evidence-based assurance practices