Principal Machine Learning Engineer Session and InPlayer

We Are Paramount  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.

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.

• 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 to 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.
• 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, Tensor Flow) and big‑data ecosystems (Spark, Beam, Databricks).

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

• Define the
  
  Roadmap:
  
  Establish a clear technical vision for the "Session" pod, prioritizing improvements to "Watch‑Next" and end‑card accuracy.
• Ship Production Gains:
  Deliver measurable increases in session duration and "Keep Watching" click‑through…