• Design and deploy ML components for channel ranking and guide personalization • Work with linear scheduling data to create features for program relevance • Use Qdrant to group similar channels • Train and iterate on models using TensorFlow/PyTorch • Own delivery of defined tasks from data exploration to production deployment

• Lead the Ad Pod: Drive the technical roadmap for all Pluto-specific ML use cases, acting as the bridge between AMLG's core infrastructure and Pluto’s product goals. • Personalized Channel Discovery: Architect retrieval and ranking systems to recommend the right linear channels to users in real-time. • Dynamic Scheduling & Creation: Develop models that optimize channel scheduling and inform the creation of "Pop-up" or algorithmic channels based on viewer trends. • Multi-Objective Optimization: Design loss functions and reward systems that weigh user commitment (watch time) against business health (ad impressions and revenue). • EPG Personalization: Lead the vision for personalizing the Electronic Programming Guide, ensuring the most relevant content is surfaced immediately upon app launch. • Cross-Functional Leadership: Partner with RevOps and Product to translate business constraints (e.g., ad delivery guarantees) into technical ML requirements.

• Architect and implement models that dynamically reorder or highlight channels within the PlutoTV EPG • Design models that assist in content scheduling decisions based on user preferences and content affinity • Deliver production-ready code in TensorFlow and PyTorch within our GCP infrastructure • Proactively identify and mitigate issues related to 'live' data streams and real-time scheduling constraints • Guide junior engineers in the nuances of Linear TV data and FAST-specific success metrics

• Lead Multi-Stage Personalization: Design and deploy retrieval and deep ranking systems specifically optimized for in-player surfaces and "Watch-Next" carousels. • Own the Session Lifecycle: Develop end-to-end ML pipelines that utilize session-based modeling and real-time user behavior to predict the next best piece of content. • Optimize for Performance: Architect systems that meet strict latency requirements necessary for in-player experiences where delays directly impact the viewing experience. • Cross-Functional Strategy: Partner with Product, Design, and Content teams to define success metrics specific to session length, "binge" rates, and playback transitions. • Scientific Rigor: Establish high-integrity experimentation practices and improve offline→online correlation for session-based rewards and contextual bandits. • Technical Mentorship: Act as a player-coach, developing technical talent within the pod and shaping the culture of the broader AMLG.