Prinicpal Machine Learning Engineer, Presentation and Visual Optimization

Paramount is on a mission to unleash the power of content. They are seeking a Principal Machine Learning Engineer to lead the Presentation pod, focusing on optimizing how content is visually displayed to enhance user engagement and drive clicks across their global streaming platforms.

Responsibilities:

• Lead the Presentation Pod: Define the technical roadmap for visual personalization, bridging the gap between ML science and UI/UX design
• Artwork & Marquee Personalization: Architect and deploy multi-armed bandit (MAB) systems to dynamically select the best creative assets for show tiles and hero marquees
• Layout & Style Optimization: Develop models to personalize carousel design types (e.g., square vs. poster), presentation styles, and even the ordering of attributes shown to the user
• Title & Copy Optimization: Utilize NLP and LLMs to experiment with and optimize carousel titles and content descriptions based on user preferences and trending topics
• Fast Experimentation Frameworks: Design "always-on" experimentation pipelines that can shift traffic based on model confidence and reward signals (CTR, Playback Start)
• Visual Understanding: Partner with Content Engineering to leverage visual embeddings and computer vision signals to understand why certain artwork performs better than others

Requirements:

• 6-8+ years of experience in machine learning engineering or applied science
• Deep hands-on experience with Multi-Armed Bandits, Contextual Bandits, Thompson Sampling, or Upper Confidence Bound (UCB) algorithms
• Proven track record of designing high-velocity A/B testing or online learning systems
• Proficiency in Python, PyTorch/TensorFlow, and big-data processing (Spark/Databricks)
• Experience leading a technical pod or team, with a focus on translating product/design needs into engineering requirements
• Experience in Visual Personalization (artwork, thumbnails, or creative optimization)
• Background in Computer Vision (OCR, aesthetic scoring, or image feature extraction)
• Knowledge of Reinforcement Learning (RL) for sequential layout optimization
• Familiarity with LLMs/Generative AI for automated copy generation and title testing