Principal ML Architect: Personalization & Discovery

Amazon; Seattle (WA); On-site; USD 199,000 - 269,000; Full time

Benefits offered by this job

RSUs

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon is seeking a Principal Applied Scientist for Prime Video to lead the invention, development, and deployment of groundbreaking AI solutions at scale. You will guide ML systems from research to production, mentoring scientists and influencing roadmaps across product and engineering teams.

The role demands deep ML expertise, especially in personalization and recommender-style systems, plus hands-on experience with Python/Java/C++.

Qualifications

• PhD (or MS with 10+ years of applied experience) in ML/CS/Statistics or related field.
• 8+ years designing, building, and deploying ML models at scale for real-world business applications.

Responsibilities

• 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.
• Provide technical leadership and influence product vision by collaborating with engineers, product managers, and senior stakeholders.
• Design and lead high-impact A/B tests and data analyses to validate hypotheses and guide product direction.
• Drive technical bar-raising across science and engineering teams through mentorship, design reviews, and collaboration.
• Publish impactful research internally and externally (e.g. top-tier conferences and journals).

Skills

Cross-functional leadership

ML at scale

Python/Java/C++

Recommender systems

AI strategy

Publications

Distributed systems

Education

PhD or MS+10 years applied ML

Tools

TensorFlow

PyTorch

Hadoop

Spark

Job description

Amazon is seeking a Principal Applied Scientist for Prime Video to lead the invention, development, and deployment of groundbreaking AI solutions at scale. You will guide ML systems from research to production, mentoring scientists and influencing roadmaps across product and engineering teams.

The role demands deep ML expertise, especially in personalization and recommender-style systems, plus hands-on experience with Python/Java/C++.