Sr. Technical Program Manager — Feed Ranking & Recommendations

Meta's Feed Ranking & Recommendations team is seeking a senior Technical Program Manager to lead one of the most technically ambitious programs in our recommendations ecosystem: unifying two historically separate large-scale ranking stacks — content-first and interest-first recommendation — into a single, end-to-end ranking system. Today these stacks maintain parallel models, pipelines, and serving paths; the future state is one common foundation spanning retrieval, model serving, value modeling, and the control layer, built on a modern recommendation-as-a-service platform. This is a rare "re-architecture in flight" program: you will drive the consolidation of production ranking systems that serve billions of people, while the systems continue to run and while topline results must hold. The work sits at the intersection of large-scale machine learning, serving infrastructure, and product impact, and requires a TPM who can go deep technically, operate in a highly ambiguous problem space, and align many engineering, infrastructure, and product teams around a shared multi-quarter roadmap. The impact you'll make: - SOTA Models: collapse duplicated models, pipelines, and tooling into one stack, materially reducing the cost and time to ship ranking improvements. - Capacity savings: consolidate serving and training footprints to reclaim significant GPU inference (and downstream training and dataset) capacity — a first-order priority in a capacity-constrained environment. - Faster, better topline: establish a single, modern lever for ranking innovation so future model and product wins land faster and with higher quality. Work complexity — what makes this role hard - Migrating live, high-scale systems: you are re-architecting production ranking systems without downtime and without regressing metrics that leadership watches closely. - Competing hard constraints: latency, capacity, launch neutrality, and delivery timelines are frequently in tension; you will broker principled tradeoffs across them. - Deep technical ambiguity: the target architecture is still being defined in parts; you must frame ambiguous problems into a concrete, sequenced plan and adjust as the technical picture evolves. - Broad, matrixed collaboration: success depends on aligning many ML, infrastructure, data, and product teams — none of which you own — around one roadmap and one set of priorities. Own the end-to-end program to unify the content-first and interest-first ranking stacks — models, retrieval, value model, and control layer — onto a common recommendation-as-a-service foundation. Drive the migration from legacy dual pipelines to consolidated content pipelines across multiple surfaces (e.g., pages and groups), sequencing the transition to minimize risk. Lead multiple parallel workstreams with dedicated engineering owners, and sequence milestone launches (e.g., a unified multi-task ranking model, followed by a unified value model) to deliver measurable half-over-half progress. Partner deeply with recommendations-infrastructure teams on the hardest technical constraints: serving latency, online/offline parity, and feature generation and extraction. Manage launch-neutrality risk head-on — land large infrastructure migrations without topline regressions despite known latency tradeoffs of the newer serving stack; design the experimentation, ramp, and validation strategy to reach statistically-significant or neutral outcomes. Own the capacity strategy for the migration end to end — model the GPU inference, training, and dataset footprint through the transition (including temporary dual-running costs) and deliver on committed capacity savings. Define the metrics and dashboards that measure program health, capacity impact, and launch quality; drive crisp, data-backed progress reporting. Communicate cross-functionally and to executives, build consensus across many teams, and proactively resolve technical and organizational blockers to keep momentum. B.S. in Computer Science or a related technical discipline, or equivalent experience 12+ years of software engineering, systems engineering, or technical program/product management experience, including 4+ years of program/product management Experience delivering large-scale technical programs in a matrixed organization, from inception through production Hands-on experience with services or systems that apply machine learning, ranking, or recommendations at scale Demonstrated ability to operate autonomously across multiple teams, with strong critical thinking and technical judgment Communication and stakeholder-management experience, including building consensus and presenting to technical leadership Direct experience with recommendation or ranking infrastructure — retrieval, model serving, value/utility modeling, feature platforms, or control/policy layers Track record of launching ML models to production with accountability for topline product metrics Experience leading consolidations, migrations, or re-platforming of large production systems without regressions Fluency in serving performance and efficiency tradeoffs (latency, throughput, online/offline parity) and in GPU inference capacity planning Experience running capacity or efficiency programs and quantifying savings Familiarity with modern generative and foundational-model approaches to recommendations 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