Technical Program Manager - Feed and Video Ranking and Recommendations

Meta Platforms, Inc.•Menlo Park, CA

34d

About The Position

Facebook's Feed and Video Ranking & Recommendation team seeks a Technical Program Manager (TPM) to lead complex, large-scale programs advancing the state-of-the-art in machine learning and AI systems for content ranking and recommendation. In this high-impact role, you will collaborate across engineering, research, product, hardware, and data center teams to design, build, and scale the foundational infrastructure and algorithms that power Meta's core user experiences. You will drive the end-to-end integration of new ML models, hardware, and infra stack-from initial design validation through production deployment-focusing on reliability, efficiency, and scalability. This includes developing repeatable frameworks for onboarding new ML platforms, ensuring robust execution, and proactively resolving technical and organizational challenges to maintain project momentum. You will leverage your technical acumen and business insight to streamline onboarding of new AI hardware and ML platforms into Meta's core ranking and recommendation infrastructure, championing best practices and driving impactful outcomes that advance Meta's AI capabilities.

Requirements

Bachelor's degree in Computer Science, Electrical Engineering, or related technical field, or equivalent experience
12+ years of experience in software engineering, hardware engineering, systems engineering, or technical product/program management, with a focus on ML/AI systems or infrastructure
Knowledge of software and hardware development for large-scale ML/AI platforms, including distributed training, inference, and hardware readiness
Proven experience delivering complex ML/AI technology programs from inception to production at scale
Demonstrated ability to define and optimize engineering processes for ML/AI infra, including onboarding, reliability, and efficiency
Demonstrated analytical and problem-solving skills for large-scale ML/AI systems
Experience building and influencing cross-functional relationships across engineering, research, hardware, and product teams
Experience defining strategic direction and identifying new opportunities for impact in ML/AI infra and platforms
Effective communication skills, with experience influencing executive leadership and technical management teams
Strong understanding of large language models, machine learning, and scaling distributed systems for AI workloads

Nice To Haves

Experience with ML/AI infra platforms (e.g., PyTorch, TensorFlow, distributed training frameworks)
Experience with onboarding and scaling new hardware platforms (GPUs, accelerators) for ML/AI workloads
Experience in open source ML/AI platform development and community engagement
Experience driving responsible and privacy-preserving AI initiatives
Experience in technical leadership roles for ML/AI infra programs at planetary scale

Responsibilities

Lead and align cross-functional program teams to deliver on ML/AI infrastructure goals, ensuring clarity of objectives and accountability
Partner with engineering, hardware, data center, and research teams to define program requirements, prioritize initiatives, and shape the roadmap for ML/AI infra and platform scaling
Drive technical strategy and execution for onboarding and scaling new AI hardware (GPUs, accelerators) and distributed ML systems, including LLMs and GenAI workloads
Develop and implement communication strategies to proactively share program status, challenges, and risks with stakeholders at all levels
Manage cross-functional dependencies and risks, adjusting scope, timeline, and resources to ensure successful delivery of ML/AI infra programs
Lead the end-to-end lifecycle of ML/AI infra programs: technical analysis, design, development, testing, deployment, and post-launch support
Establish and track key metrics, benchmarks, and performance indicators for ML/AI infra, driving accountability and continuous improvement
Anticipate and address complex, long-term infrastructure challenges in partnership with engineering and research leaders
Drive product and infra strategy to support Meta's AI/ML initiatives, including responsible and privacy-preserving AI
Champion process improvements and automation to streamline workflows and reduce manual effort across ML/AI infra teams
Foster innovation, reliability, and efficiency in scaling ML/AI systems at Meta
Commitment to scaling infrastructure for large-scale ML/AI systems, including privacy, security, and responsible AI practices