Speaker: Janani Narayanan
Applied ML Engineer @Uber, Previously Tech Lead on DynamoDB Control Plane (Early Stage), 10+ Years Tech Industry Experience
Janani is a Senior Staff Engineer and Technical Lead Manager at Uber Technologies, where she has been at the forefront of transformative zero-to-one (0-1) initiatives for the past nine years. Prior to her current role, she was a pivotal contributor to SimpleDB and DynamoDB, playing a key role in their development during the initial four years post-public launch. Her work has led to several patents across both DynamoDB and Uber.
At Uber Eats, Janani leads a specialized "tiger team" focused on addressing the cold start problem by understanding selection. Her team's mission is to solve challenges related to eater and merchant retention by improving the precision of machine learning models that drive eater targeting, merchant acquisition, growth, personalization, ranking, and recommendations. Previously, she has successfully scaled UberEats’s Ads, Feed, and Search backends by 3x while maintaining low latency, optimized global rider pricing through advanced ML pipelines, and developed precise ETA predictions for accurate upfront pricing.
Janani has also built foundational infrastructure for anomaly detection in fares, created targeted rider promotions powered by machine learning, and developed distributed event processing systems capable of handling trip-scale data. These systems are crucial for real-time and batch processing, underpinning Uber’s real-time machine learning predictions across its marketplace, finance, and accounting functions.
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Session
Optimizing Search at Uber Eats
Uber has an in-house search engine called Search In Action (SIA). As the backbone behind the feed and search capabilities of Uber's Delivery business, SIA plays a crucial role in expanding selection seamlessly for customers which is a strategic advantage to the business.