Senior Pmt Es - Reinforcement Learning, Sagemaker AI

Amazon Amazon · Big Tech · Bellevue, WA · Project/Program/Product Management--Technical

Senior Product Manager, Technical to define and own the product strategy for reinforcement learning (RL) on Amazon SageMaker AI. The role involves shaping how customers leverage RL for foundation model alignment, customization, and improvement, making RL more accessible for a broad range of customers.

What you'd actually do

  1. Define and execute product strategy and roadmap for reinforcement learning on SageMaker AI, working backwards from customer needs in model alignment, optimization, and decision-making
  2. Partner with applied science and engineering teams to define technical requirements for training infrastructure, algorithms, and tooling
  3. Own business metrics including adoption, usage, and revenue growth
  4. Engage directly with customers—from ML researchers at startups to enterprise AI teams—to understand their workflows and pain points
  5. Create product narratives, product specifications, and business cases for new initiatives

Skills

Required

  • Bachelor's degree in computer science, engineering, math, finance, or economics
  • 5+ years of technical product management with internet business experience
  • Experience working with customers, technical teams, and management to collect requirements, describe software product features, and technical designs
  • Experience in taking a product from conception & definition phase through engineering design and taking it to market

Nice to have

  • Experience delivering large-scale SaaS, PaaS or LaaS products where you are responsible for the full product lifecycle, from concept through GTM (go to market)

What the JD emphasized

  • reinforcement learning
  • foundation models
  • AI systems
  • model alignment
  • optimization
  • decision-making systems
  • SageMaker AI

Other signals

  • Define and execute product strategy and roadmap for reinforcement learning on SageMaker AI
  • Partner with applied science and engineering teams to define technical requirements for training infrastructure, algorithms, and tooling
  • Engage directly with customers—from ML researchers at startups to enterprise AI teams—to understand their workflows and pain points