Jun 2017 – Present · 8 yrs 11 mos · Menlo Park · Hybrid

• 1. AI Infra - PyTorch Inference Enablement for RecSys
• Post training model optimizations to scale sparse / sequential arch of the generative recommendation models for GPU distributed inference at massive scale (10+TB).
• 2. Ads ML & AI Infra - Feature and Training Data Infra
• TL within Realtime Feature Infra team where I worked on improving feature freshness, enabling new feature paradigms and modernizing company wide feature infra stack to deliver huge product wins across Ads, Feeds, IG and Integrity teams.
• Rearchitected our realtime/streaming feature infrastructure for Ads Ranking to improve feature freshness from 10+ min -> < 10s leveraging Kappa architecture and deliver $100 million+ revenue YoY.
• Enhanced the feature platform to support generation and serving of near realtime (10+ min freshness) graph learning (e.g. PPR, GNN etc) features widely used for user representation and ads ranking use cases (user features, ads related to a specific ad etc).
• Modernized the recommendation ML feature platform with rich set of feature paradigms (event based features, topK, latestN etc), achieving wide adoption across multiple FB/IG products and contributing to significant product metrics wins (e.g. Reels watch time>18%, Facebook global session ~2%, IG Session > 0.11%, Feed VPV > ~2%).
• Developed several key capabilities to modernize the feature infrastructure for Integrity teams at Meta, including support for new operators, feature sharing, and ensuring seamless integration with training platforms
• 3. Ads Realtime Data Infra - Audience Infra
• Dynamic re-sharding and Elias-Fano encoding of the data to deliver 30% storage optimization for 1+ PB data, 30%+ memory improvements, 20%+ CPU utilization and reducing the overall service start time from 1+ day to a few hours.
• Supported new search query patterns e.g. filtering and aggregating data based on fact tables that can be combined with search queries.