Principal PMT - Personalization ML Platform, Prime Video Personalization & Discovery

Description

Prime Video is seeking a Principal Product Manager Technical to drive the vision, strategy, and execution of our Machine Learning platform and infrastructure, powering personalization, discovery, and customer and content intelligence at global scale.

Prime Video is seeking a Principal Product Manager Technical to drive the vision, strategy, and execution of our Machine Learning platform and infrastructure, powering personalization, discovery, and customer and content intelligence at global scale. In this role, you will own the strategy and roadmap for foundational platforms across Data, ML, and Measurement, enabling teams to rapidly build, experiment, and product ionize machine learning solutions.

Your mission is to enable ML builders to move from idea to production faster by removing bottlenecks across the ML lifecycle from data ingestion and feature engineering to model training, deployment, and experimentation so teams can iterate faster while providing robust measurement capabilities that shorten iteration cycles. You will own the connection between platform capabilities and end-customer experience improvements, defining how infrastructure investments enabling Product teams to drive measurable gains in personalization quality, content discovery, and customer engagement metrics.

You will operate in highly complex domains include recommendation systems, real‑time inference, large‑scale distributed training, LLM infrastructure, and partner closely with engineering and science leaders to influence architecture, drive adoption, and deliver measurable business outcomes.

• Define and drive the multi‑year vision and roadmap for ML platform and infrastructure, articulating how platform decisions directly translate to customer‑facing personalization outcomes across discovery, content intelligence, and emerging GenAI experiences
• Develop and own the data platform strategy, delivering clean, curated datasets that encode business logic and provide science teams with trusted, model‑ready data foundations
• Build end‑to‑end ML tooling and pipelines that enable ML builders to discover datasets, ideate modeling approaches, conduct research and experimentation, and seamlessly product ionize models from prototype to production
• Support large‑scale model training, real‑time inference, and compute optimization across CPU and GPU to ensure models perform efficiently at production scale
• Define and evolve the feature store strategy, supporting both online and offline data serving while minimizing online/offline skew to ensure consistency between training and serving environments
• Own the developer experience for ML builders by delivering self‑service tools, reusable components, and standardized workflows that increase productivity, reduce operational overhead, and improve cost efficiency, scalability, and availability
• Enhance the measurement platform to monitor model performance in production, track input and output metric alignment, and provide clear visibility into how platform capabilities contribute to customer‑facing outcomes
• Influence senior leadership and cross‑functional stakeholders to align on platform strategy, investment priorities, and trade‑offs
• Drive adoption of platform capabilities across teams by ensuring solutions are intuitive, reliable, and measurably better than existing workflows.

• 7+ years of end to end product delivery experience
• 4+ years of technical product or program management experience
• Bachelor's degree
• Experience with feature delivery and tradeoffs of a product
• Experience owning/driving roadmap strategy and definition
• Experience leading engineering discussions around technology decisions and strategy related to a product
• 3+ years of working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage experience
• Deep understanding of MLOps, model training, evaluation metrics and data pipelines.

• Experience in project management methodologies, business analysis, or process improvement
• Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference life cycles, and optimization of model execution, or experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
• Familiarity with recommendation systems is a bonus
• Fluency in technology alternatives with ability to weigh pros and cons of different technical approaches

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