ML Innovation for Products, PhD Research Intern

The ML Innovation for Products (MLIP) team is an applied machine learning and research team within Meta's Ranking & Foundational AI org, focused on the state-of-the-art (SOTA) 0-to-1 ML that powers retrieval, ranking, and auction across Meta's ads systems. This is a very unique team that functions like an applied research lab with a production deploy button: we imagine, evaluate, prototype, and ship 10x ideas into one of the largest recommender systems in the world, a system that serves billion-scale users every single day and is the engine behind Meta's revenue. As a horizontal team, we have the unique leverage to develop SOTA techniques alongside expert engineers and researchers, then scale them into the head models powering Facebook, Instagram, and beyond. What you would work on Because the team works at the boundary of research and production, our research and its applications are intertwined, and much of the work is published at major ML and recommender systems conferences. Some of the work the team is involved in includes: Memora, a new scaling paradigm for ads recommendation inspired by memory research in LLMs. Learned representations in large recommenders turn out to be highly concentrated, so much of the input signal is effectively ignored and further architectural scaling yields flattening returns. We build memory modules that store and retrieve historical model-generated representations and inject them back as differentiating signals, improving model quality without proportionally scaling compute. Two workstreams: Learning from Memory (LFM) and Enriched Ad Sequences (EAS). Graph Learning at billion-node scale. We build heterogeneous graph representations of users, ads, and their interactions, including user journey graphs, and we push the modeling frontier: k-hop induced subgraph methods, temporal GNNs, graph transformers, and deeper architectures with residual and spectral rewiring to counter over-smoothing and over-squashing. …

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