Principal Machine Learning Engineer, Session and In-Player Experience

Paramount•New York, NY

20h

About The Position

We are looking for a Principal Machine Learning Engineer to lead our "Session" pod - the team in charge of the critical mission of "Keeping the Session Going." While other teams focus on the initial click, your team owns the viewer's journey after playback starts. You will lead a high-impact pod of Senior ML engineers and Applied Scientists to architect the systems powering in-player browse, "Watch-Next" recommendations, and end-cards across Paramount+ and Pluto TV. This is a highly visible technical leadership role where you will define the strategy for real-time, post-playback discovery. Because these decisions happen while a user is already engaged in content, you will navigate unique challenges in ultra-low latency inference, session-based behavior modeling, and distinct UX constraints that differ significantly from standard home-page ranking. The "Session" pod is the engine behind long-term retention and viewer satisfaction. In this role, you will directly shape: The Post-Playback Journey: Owning the "Watch-Next" and end-card algorithms that identify if a user stays for another hour or leaves the platform. In-Player Discovery: Designing seamless in-player browse experiences that allow users to explore content without interrupting their current stream. Real-Time Intelligence: Building the models that react to session-level signals in milliseconds to provide truly dynamic personalization.

Requirements

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

Nice To Haves

Experience with in-player or post-playback recommendation domains.
Background in multi-modal signals (e.g., using video/audio features for "Watch-Next" similarity).
Knowledge with Vector Search integration and embedding pipeline optimization.
Experience bridging the gap between Core Science teams and production-facing Product teams.

Responsibilities

Lead Multi-Stage Personalization: Design and deploy retrieval and deep ranking systems specifcally 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.

Benefits

Attractive compensation and comprehensive benefits packages.
Generous paid time off.
An exciting and fulfilling opportunity to be part of one of Paramount’s most dynamic teams.
Opportunities for both on-site and virtual engagement events.
Unique opportunities to make meaningful connections and build a vibrant community, both inside and outside the workplace.
medical
dental
vision
401(k) plan
life insurance coverage
disability benefits
tuition assistance program
PTO
bonus eligible