Principal PM: Personalization ML Platform & GenAI

Amazon Inc.; Seattle (WA); On-site; USD 180,000 - 243,000; Full time

Benefits offered by this job

RSUs

Health insurance

401(k) matching

Job summary

Amazon.com Services LLC in Seattle is seeking a Principal Product Manager Technical to lead the vision, strategy, and execution of our ML platform and infrastructure, powering personalization, discovery, and customer and content intelligence at global scale.

You will own the strategy and roadmap for foundational platforms across Data, ML, and Measurement, enabling teams to rapidly build, experiment, and productionize machine learning solutions, moving from idea to production faster while

Qualifications

• 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 lifecycles, 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

Responsibilities

• 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 productionize 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

Skills

Product delivery

Technical PM

Data & AI

Roadmap strategy

Stakeholder mgmt

ML/LLM basics

Education

Bachelor's degree

Job description

Amazon.com Services LLC in Seattle is seeking a Principal Product Manager Technical to lead the vision, strategy, and execution of our ML platform and infrastructure, powering personalization, discovery, and customer and content intelligence at global scale.

You will own the strategy and roadmap for foundational platforms across Data, ML, and Measurement, enabling teams to rapidly build, experiment, and productionize machine learning solutions, moving from idea to production faster while