Lead Machine Learning Engineer, Entry and Re-engagement

#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 Senior Lead Machine Learning Engineer for the Entry pod. This team owns the high stakes surfaces that establish whether a user stays or bounces within seconds of opening the app. You will lead the ML strategy for Onboarding, Re‑entry, and High‑Commitment Recommendations, including core features like 'Your Next Watch', 'Jump Back In', and 'While You Were Away'.

The technical task of this pod is optimizing start rates under high uncertainty. You will design systems that oversee the "cold start" problem for new users and the "intent gap" for returning users. Because these surfaces share similar failure modes such as over‑indexing on recency or failing to surface 'must‑watch' content. You will architect an unified approach to re‑engagement that balances historical preference with real‑time context.

The Entry pod owns the "Moment of Truth." In this role, you will directly shape:

• The First Touchpoint:
  Building the onboarding models that turn a first‑time visitor into a long‑term subscriber.
• The Re‑entry Loop:
  Perfecting the 'Jump Back In' experience to ensure users can resume their journey with zero friction.
• High‑Stakes Discovery:
  Owning 'Your Next Watch' (YNW), the primary engine for transitioning a user from a finished series into their next obsession.

• Entry Pod:
  Contribute to the technical vision for re‑engagement and onboarding, leading a pod of senior engineers to deliver high‑impact production models.
• Optimize Re‑entry Surfaces:
  Architect models for 'Jump Back In' (JBI) and 'While You Were Away' (WYWA) that account for temporal decay, episode progress, and cross‑device signals.
• Solve the Cold Start Problem:
  Develop advanced onboarding algorithms that use minimal metadata and global trends to provide high‑quality recommendations to new users immediately.
• Architect Hybrid Retrieval Systems:
  Develop multi‑stage retrieval pipelines that successfully merge traditional feature‑driven methods with semantic vector search, ensuring seamless integration with downstream ranking models for optimal performance.
• Design High‑Commitment Ranking:
  Lead the development of the 'Your Next Watch' (YNW) engine, optimizing for long‑form commitment rather than just a click.
• Model Uncertainty:
  Implement exploration/exploitation strategies to navigate the uncertainty of user intent during app entry.
• Unified Success Metrics & System Performance:
  Define and optimize for Start Rate, Time to Play, and Day‑1 retention. Beyond business KPIs, you will own the system‑level Service Level Objectives (SLOs), ensuring high throughput and low‑latency delivery of recommendations in production.

• 6‑8+ years of experience in machine learning engineering, specifically in ranking, retrieval, or reinforcement learning.
• Cold Start
  
  Experience:
  
  Proven experience building recommendation systems that perform under data sparsity or for 'new‑to‑system' entities.
• Advanced Ranking:
  Deep knowledge of multi‑stage ranking, learning‑to‑rank (LTR), and handling temporal features in real‑time.
• High‑Throughput Engineering:
  Deep expertise in designing and deploying scalable hybrid retrieval architectures capable of processing massive interaction volumes in real‑time.
• Rigorous Experimentation:
  Proficiency in A/B testing and the design of complex online metric frameworks, including primary, secondary, and guardrail indicators to validate model impact.
• Data Infrastructure:
  Proficiency in leveraging modern processing frameworks like Spark, Beam, or Big Query to engineer features and product ionize machine learning models at massive scale.
• Cros…