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Pratyusha Singaraju

Pratyusha Singaraju

Pratyusha Singaraju is a Senior Software Engineer at Netflix Inc., where she architects and builds sophisticated backend services that power the content understanding infrastructure behind Netflix's search, collections, and ratings systems. Her work directly impacts how millions of users discover and engage with content across the platform. Prior to Netflix, Pratyusha was part of Microsoft's data team for Bing, where she worked on knowledge graph initiatives spanning diverse public domains, including food, nutrition, politics, events, and building an enterprise knowledge graph.
Netflix, USApratyusha-singaraju
Orchestrating Content Workflows at Netflix Scale
JSNation 2026JSNation 2026
Upcoming
Orchestrating Content Workflows at Netflix Scale
Every title that reaches Netflix's millions of subscribers passes through a gauntlet of decisions — some made by rules, some by ML models, and some by human reviewers. And with Netflix's catalog growing faster than ever, getting all of them to work together reliably, at scale, is one of the hardest problems in production systems engineering.In this talk, we'll share how we rethought workflow orchestration from the ground up to build a framework where rule-based automation, ML models, and human-in-the-loop review aren't just bolted together — they're first-class citizens in the same pipeline. We'll get into the real challenges: routing decisions across heterogeneous components, isolating failures so a single bad signal doesn't cascade, and closing the feedback loop across the entire system. Along the way, we'll show why the architectural choices that make this work today are exactly what make AI agent integration tomorrow feel like a natural evolution — not a retrofit.If you've ever tried to build a production pipeline that doesn't fall apart when one piece changes — this one's for you.
architecture
Orchestrating Content Workflows at Netflix Scale
Web Engineering Summit 2026Web Engineering Summit 2026
Upcoming
Orchestrating Content Workflows at Netflix Scale
Every title that reaches Netflix's millions of subscribers passes through a gauntlet of decisions — some made by rules, some by ML models, and some by human reviewers. And with Netflix's catalog growing faster than ever, getting all of them to work together reliably, at scale, is one of the hardest problems in production systems engineering.In this talk, we'll share how we rethought workflow orchestration from the ground up to build a framework where rule-based automation, ML models, and human-in-the-loop review aren't just bolted together — they're first-class citizens in the same pipeline. We'll get into the real challenges: routing decisions across heterogeneous components, isolating failures so a single bad signal doesn't cascade, and closing the feedback loop across the entire system. Along the way, we'll show why the architectural choices that make this work today are exactly what make AI agent integration tomorrow feel like a natural evolution — not a retrofit.If you've ever tried to build a production pipeline that doesn't fall apart when one piece changes — this one's for you.
architecture