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.

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.

Principal Machine Learning Engineer – Presentation and Visual Optimization

Key skills