Sr Machine Learning Engineer

Sr Machine Learning Engineer

45531 New York, NY, US, 10036 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 seeking a Senior Machine Learning Engineer to join the PlutoTV pod. Your "North Star" mission is the personalization of the Linear/FAST experience, specifically focusing on Channel presentation, scheduling, and guide personalization. You will bridge the gap between traditional TV and modern ML, ensuring the Electronic Programming Guide (EPG) feels dynamic and tailored to every viewer. As a Senior Engineer, you will lead the design of complex scheduling and ranking features in our GCP-based stack. You will tackle the unique assignment of personalizing content that is tied to a specific time slot, utilizing TensorFlow and PyTorch to predict what a user wants to watch now versus what they might want to watch in an hour.

Why This Role Matters Reinventing the Grid: You shift the PlutoTV experience from a static grid to a personalized discovery engine, significantly reducing "Time to Content." Scheduling Intelligence: You build the models that establish how channels are scheduled to maximize viewership and engagement. FAST Evolution: You apply modern ML techniques to the fastest-growing sector of the streaming market, defining how linear TV is consumed in the AI era.

Key Responsibilities Guide Personalization: Architect and implement models that dynamically reorder or highlight channels within the PlutoTV EPG. Scheduling Optimization: Design models that assist in content scheduling decisions based on user preferences and content affinity. Multi-Framework Development: Deliver production-ready code in TensorFlow and PyTorch within our GCP infrastructure. Anticipate System Risks: Proactively identify and mitigate issues related to "live" data streams and real-time scheduling constraints. Technical Mentorship: Guide junior engineers in the nuances of Linear TV data and FAST-specific success metrics.

Basic Qualifications 5+ years of experience in MLE; knowledge with GCP; proficiency in TensorFlow and PyTorch. Additional Qualifications Direct experience with FAST apps or linear TV scheduling; experience with "Always-on" streaming data