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Machine Learning Engineer, Ads PersonalizationParamount Global Services • United States
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Machine Learning Engineer, Ads Personalization

Paramount Global Services
  • US
    United States
  • US
    United States
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Über

Machine Learning Engineer, Ads Personalization
Technology New York Full-Time Fully Remote #WeAreParamount on a mission to unleash the power of content… you in? We've got the brands, we've got the stars, we've got the power to achieve our mission to entertain the planet – now all we're missing is… YOU! Becoming a part of Paramount means joining a team of passionate people who not only recognize the power of content but also enjoy a touch of fun and uniqueness. Together, we co-create moments that matter – both for our audiences and our employees – and aim to leave a positive mark on culture. Overview We are seeking a Machine Learning Engineer to join the PlutoTV pod. You will focus on Channel, Guide, and Schedule personalization. Your goal is to make the FAST (Free Ad-supported Streaming TV) experience feel as dynamic as a premium on-demand service. You will help build the models that select which channels appear in the guide and how content is scheduled to maximize viewer session length. This role requires deep knowledge of FAST systems and the unique constraints of linear programming. You will implement features within our GCP stack, leveraging Qdrant for content similarity and Post-training RL to optimize the "Lean-back" viewing experience. Why This Role Matters Modernizing Linear TV: You are part of the team transforming the static EPG (Electronic Programming Guide) into a personalized, data-driven discovery engine. Optimizing the "Lean-Back": Your models help users find something to watch immediately, reducing the friction inherent in "channel surfing." Global Scale: PlutoTV is a leader in the FAST space; your code will impact the viewing habits of millions of users worldwide. Key Responsibilities Feature Development: Design and deploy ML components for channel ranking and guide personalization. FAST Metadata Integration: Work with linear scheduling data to create features that account for "Current" vs "Next" program relevance. Vector-Based Similarity: Use Qdrantto group similar channels and content to improve the "More Like This" experience within the guide. Model Refinement: Train and iterate on models using TensorFlow/PyTorch, focusing on session-start and watch-time metrics. Independent Execution: Own the delivery of defined tasks within the PlutoTV roadmap, from data exploration to production deployment. Basic Qualifications Minimum: 3+ years in MLE; proficiency in Python/SQL; experience with TensorFlow/PyTorch and GCP. Additional Qualifications Preferred: Knowledge with FAST or Broadcast TV data structures; experience with Qdrant or similar vector databases.
  • United States

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  • English
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