Meta Ads Ranking seeks a Technical Program Manager (TPM) to lead complex, large-scale programs advancing our infrastructure and platforms with the state-of-the-art in machine learning and AI systems for ad ranking. In this high-impact role, you will collaborate across engineering, product, data science, production teams to design, build, and scale the foundational advertising infrastructure and algorithms that power Meta’s monetization engines, one of the largest digital advertising platforms in the world. As a Technical Program Manager, you will optimize the end-to-end ML development lifecycle, from experimentation and exploration to large-scale production deployment, with an focus on system reliability, hardware efficiency, and performance at an large scale. You will act as a primary catalyst for innovation, establishing repeatable frameworks to accelerate the onboarding of next-generation AI hardware and ML platforms. By leveraging deep technical expertise and executive-level business acumen, you will bridge organizational boundaries, resolve complex technical bottlenecks, and champion the industry-standard practices that solidify Meta's position at the forefront of AI-driven monetization.
Responsibilities
Orchestrate and align mission-critical cross-functional teams to deliver against ambitious ML/AI infrastructure objectives, ensuring absolute clarity and executive accountability Forge strategic partnerships with senior engineering, hardware, and research leadership to define long-term roadmaps for platform scaling and infrastructure prioritization Drive the technical execution for the integration of next-generation AI accelerators (GPUs, ASICs) and distributed machine learning systems Design and implement high-level communication frameworks to synthesize program health, risk profiles, and strategic shifts for executive leadership and global stakeholders Mitigate complex cross-functional dependencies and systemic risks by dynamically re-engineering scope and resources to maintain project momentum and on-time delivery Command the end-to-end lifecycle of infrastructure programs, encompassing deep technical analysis, architectural design, and rigorous production deployment Engineers and track mission-critical success metrics and performance benchmarks for ML infrastructure, fostering a culture of continuous improvement and technical rigor Anticipate and address complex, long-term infrastructure challenges in partnership with engineering and research leaders Advance Ads Ranking strategy by integrating state-of-the-art AI techniques into the core architecture of monetization systems Champion process improvements and automation to streamline workflows and reduce manual effort across ML/AI infrastructure teams Foster innovation, reliability, and efficiency in scaling ML/AI systems at Meta
Qualifications
Bachelor’s degree in Computer Science, Electrical Engineering, or related technical field, or equivalent experience 12+ years of leadership experience in software, hardware, or systems engineering and technical program management, with deep expertise in ML infrastructure, monetization platforms, or large-scale recommendation systems Proven track record of delivering complex ML/AI technology programs from architectural inception through production deployment at scale Proven experience delivering complex ML/AI technology programs from inception to production at scale Exceptional ability to transform ambiguous technical challenges into actionable, high-impact strategies, with a history of driving systemic improvements in reliability, efficiency, and developer velocity Demonstrated analytical and problem-solving skills for large-scale ML/AI systems Strong executive presence with a proven ability to synthesize deep technical complexity into clear strategic narratives for leadership Effective communication skills, with experience influencing executive leadership and technical management 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) Hands-on understanding of large language models, machine learning, and scaling distributed systems, specifically within the context of high-stakes advertising auction dynamics and revenue-critical infrastructure Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)