Principal Machine Learning Engineer, Short-form

Principal Machine Learning Engineer, Short-form

Technology New York Full-Time Fully Remote #WeAreParamount 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.

Overview We are looking for a Principal Machine Learning Engineer to set the technical direction for the Shortform pod. Your mission is to define the long-term architecture and modeling strategy for the Personalization of Short-form experiences, including the "in-view" and "in-carousel" surfaces across our "Gist" and clip ecosystem. You will own the technical vision for how short-form assets convert casual browsers into committed viewers at platform scale. This is a Principal role, meaning you are the senior-most technical authority on the pod. You set multi-quarter technical strategy, drive cross-pod alignment, and are accountable for the scientific rigor of how we model long-term user satisfaction. You will work within a GCP-based environment, utilizing TensorFlow and PyTorch, with a heavy focus on Post-training RL (Reinforcement Learning) to optimize session-level and long-horizon rewards.

Why This Role Matters Setting the Architecture: You define the multi-stage ranking and RL architecture that determines which short-form asset is surfaced to every user – directly shaping CTR, discovery velocity, and long-term retention. Beyond the Click: You establish how we frame and optimize long-horizon reward signals, ensuring short-form content drives durable engagement rather than short-term engagement traps. Org-Level Quality Bar: You raise the technical bar across the Shortform pod and adjacent pods, anticipate systemic risks (data, modeling, feedback loops), and influence the broader Applied ML roadmap.

Key Responsibilities

• Set Technical Strategy: Own the multi-quarter technical roadmap for short-form ranking, candidate generation, and Post-training RL.
• Architect End-to-End Systems: Design the multi-stage ranking architecture spanning retrieval, ranking, re-ranking, and RL-based policy optimization.
• Advance reinforcement learning in production. Drive the use of post-training reinforcement learning techniques, including reward modeling, off-policy evaluation, and policy alignment to improve user satisfaction over long periods.
• Cross-Pod Influence: Partner with Content Understanding, ML Platform, Core Science, and Product to align short-form personalization with broader Discovery strategy.
• Operate at Scale: Ensure ranking pipelines are high-throughput, reliable, and observable in GCP using TensorFlow/PyTorch.
• Mentorship & Talent: Mentor IC1–IC3 engineers, set technical standards across the pod, and grow the next generation of senior ML talent.
• Mitigate Systemic Risk: Identify and resolve feedback loops, exposure biases, and filter-bubble dynamics in how short-form content is surfaced.

Basic Qualifications Minimum: 8+ years of experience in MLE with a track record of setting technical direction for large-scale ranking or recommender systems deep expertise in Reinforcement Learning, particularly Post-training RL and long-horizon reward modeling proficiency in GCP, TensorFlow, and PyTorch; demonstrated ability to influence technical strategy across multiple teams

Additional Qualifications Experience in video-first social or streaming apps background in multi-modal signal processing published work or recognized contributions in ranking, RL, or recommender systems.