Principal Applied Scientist, Prime Video Personalization & Discovery

Amazon Amazon · Big Tech · Sunnyvale, CA · Applied Science

Principal Applied Scientist role at Prime Video focused on inventing, developing, and deploying AI solutions for personalization and discovery. The role involves technical and strategic leadership, guiding ML systems from research to production, and mentoring scientists. Key responsibilities include prototyping and productionizing large-scale AI solutions using deep learning, generative AI, RL, and optimization, providing technical leadership, designing A/B tests, driving technical bar-raising, and staying ahead of industry trends. The team focuses on creating a highly personalized content discovery experience using ML and Generative AI.

What you'd actually do

  1. Invent, prototype, and productionize large-scale AI solutions across Prime Video’s personalization and discovery ecosystem using deep learning, generative AI, reinforcement learning, and optimization techniques;
  2. Provide technical leadership and influence product vision by collaborating closely with engineers, product managers, and senior stakeholders;
  3. Design and lead high-impact A/B tests and data analyses to validate hypotheses and guide product direction;
  4. Drive technical bar-raising across science and engineering teams through mentorship, design reviews, and collaboration;
  5. Stay ahead of industry trends and emerging research; leverage them to evolve long-term strategy and architecture;

Skills

Required

  • PhD (or Master’s with 10+ years of applied experience) in machine learning, computer science, statistics, or a related field
  • 8+ years experience designing, building, and deploying ML models for real-world business applications at scale
  • Proven leadership in driving cross-functional ML/AI initiatives from ideation through production
  • in Python, Java, or C++, and experience with ML frameworks (e.g., TensorFlow, PyTorch)
  • Deep expertise in recommendation systems, search relevance, or large-scale personalization systems

Nice to have

  • Strong publication record in top-tier conferences (e.g. NeurIPS, ICML, KDD, RecSys, SIGIR)
  • Experience with large-scale distributed systems (e.g. Hadoop, Spark)
  • Demonstrated ability to influence technical roadmaps and mentor other scientists and engineers
  • Experience defining and delivering strategic AI vision for consumer-facing products

What the JD emphasized

  • Deep expertise in recommendation systems, search relevance, or large-scale personalization systems
  • Proven leadership in driving cross-functional ML/AI initiatives from ideation through production
  • Strong publication record in top-tier conferences (e.g. NeurIPS, ICML, KDD, RecSys, SIGIR)

Other signals

  • personalization
  • recommendation systems
  • generative AI
  • reinforcement learning
  • optimization techniques