Philo operates a streaming service spanning 70+ live channels and 75,000+ on-demand titles, backed by a data and ML infrastructure built on TensorFlow, PyTorch, Snowflake, and SageMaker. The tech stack and active projects reveal a company investing heavily in recommendation engines and content personalization—core levers for engagement in a crowded streaming market. Recent hiring velocity is accelerating in senior engineering and data roles, suggesting a push to scale recommendation systems and optimize infrastructure costs across a petabyte-scale data lake.
Philo is a streaming television service offering live broadcast channels (AMC, BET, Discovery, Food Network, HGTV, Hallmark, Lifetime, MTV, Nickelodeon, and others) alongside an on-demand library, with optional premium bundles including HBO Max, AMC+, and discovery+. The service operates on a subscription model with no long-term contracts and serves millions of users across Android, iOS, Roku, Fire TV, and tvOS. The engineering org is focused on next-generation multi-screen playback, Android TV and mobile apps, and a GraphQL-powered home-page framework. Operationally, the company is managing a petabyte data lake and data warehouse infrastructure while optimizing per-query costs and upgrading storage to Apache Iceberg.
Philo's stack includes Python, Kotlin, TensorFlow, PyTorch, GraphQL, Snowflake, SageMaker, Apache Spark, Segment, and deployment across Android, Roku, Fire TV, and tvOS. They are adopting Apache Iceberg for data storage.
Active projects include a recommendation engine, ML pipeline for content personalization, next-generation multi-screen playback, Android TV and mobile apps, a GraphQL home-page framework, and infrastructure work on data warehouse upgrades and Apache Iceberg adoption for cost optimization.
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