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Paramount
Lead Machine Learning Engineer, Entry and Re-engagement
Paramount
New York•Hybrid•6-9 Yrs•Jul 8
Job description
Hybrid
Overview
#WeAreParamount on a mission to unleash the power of content… you in?
Responsibilities
- 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.
Qualifications
- 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 BigQuery to engineer features and productionize machine learning models at massive scale.
- Cross-Functional Management: Demonstrated ability to drive technical initiatives collaboratively with Product, Engineering, and Data Science partners to deliver on shared business objectives.
- Technical Focus: Mastery of Python, Neural networks (Pytorch, tensorflow, JAX), Distributed systems (Ray or Spark), Orchestration (e.g., Airflow, Argo Workflows), MLOps (MLFlow/ Weights and Biases), DevOps (Docker, Kubernetes), Ranking, Search, Recommender systems, Java (nice to have), and Serving Frameworks (Nvidia Triton).
- Mentorship: Experience mentoring junior engineers or leading technical initiatives, with a track record of spearheading complex ML projects from research to global production.
- Background in Exploration/Exploitation (Bandits) for handling user uncertainty.
- Knowledge with Causal Inference to distinguish between organic re-entry and model-driven lift.
- Advanced proficiency in re-ranking methodologies focused on balancing content diversity with top-tier relevance.
Benefits
- Attractive compensation and comprehensive benefits packages. Check out our full list of benefits here: https://www.paramount.com/careers/benefits
- 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.
- Explore life at Paramount: https://www.paramount.com/careers/life-at-paramount