Lead Machine Learning Engineer

This is a Full Time Job

#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.

Role Overview
Paramount Streaming is building the next generation of personalization and discovery across global platforms. This role leads high-impact machine learning initiatives focused on optimizing the Paramount sign-up and Pluto TV registration flow.

This position shapes how millions of viewers discover films, series, live sports, and news on Paramount , Pluto TV, and our future streaming products.This is a highly visible technical leadership role facilitating innovation at the intersection of ML modeling, retrieval and ranking systems, multi-modal embeddings, multi-armed bandits, experimentation, and real-time user comprehension.

Responsibilities
• Lead the design and development of personalization in the P sign-up flow and Pluto TV registration flows.
• Own end-to-end machine learning pipelines–from data and feature engineering to training, deployment, serving, and monitoring.
• Partner closely with product, design, content, platform engineering, and data science to define roadmaps and deliver measurable user outcomes.
• Advance our semantic search and browse experience through state-of-the-art embeddings, query understanding, and domain-specific model architectures.
• Establish high-integrity experimentation practices, improve offlineâ†'online correlation, and guide feature rollouts with strong scientific rigor.
• Mentor engineers and scientists, develop technical talent, and help shape the culture of our growing Applied ML organization.

Basic Qualifications
• 7–10 years of experience in machine learning engineering, applied science, recommender systems, multi-armed bandits, or large-scale search/ranking systems.
• Demonstrated expertise deploying ML systems in high-traffic, real-time production environments.
• Deep knowledge of modeling techniques such as representation learning, multi-task learning, multi-modal embeddings, contextual bandits, and session modeling.
• Strong fluency in experimentation methodology, A/B testing, causal reasoning, and metric design.
• Experience leading and mentoring technical teams; ability to drive strategy while remaining hands-on.
• Proficiency with modern ML tooling (PyTorch, TensorFlow), big-data environments (Spark, Beam, BigQuery), and production MLOps workflows.

#LI-PV1

Additional Information