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.

6–8+ years of experience in machine learning engineering, recommender systems, or large-scale ranking.
Demonstrated success deploying ML systems in high-traffic, low-latency production environments.
Deep knowledge of session modeling, representation learning, and contextual bandits.
Experience leading and mentoring senior technical teams with the ability to drive strategy while remaining hands-on.
Proficiency with modern ML frameworks (PyTorch, TensorFlow) and big-data ecosystems (Spark, Beam, Databricks).

Principal Machine Learning Engineer, Session and In-Player Experience

Key skills