Drimva

Amazon

Sr. Principal Scientist, Secure Work Enablement

$240K–$350K

Seattle, WA +1 · Principal · Full-time · Posted Oct 6, 2025

Location
Seattle, WA
New York, NY
Experience
15+ years of relevant, broad research experience after PhD degree or equivalent (preferred).
Education
Graduate degree in Computer science/Math or related field.

About the job

Leads applied AI research and development for secure workplace products, taking generative AI and LLM capabilities from research to production and setting the organization’s long-term science vision.

What you'll do

  • Lead cross-team research and development, translating business and functional requirements into deliverables and production-ready models.
  • Develop product experiences and approaches for human-agent collaboration, natural-language interfaces, and workplace productivity.
  • Set science strategy, identify opportunities to improve team efficiency, and collaborate with internal Amazon teams and academic partners.

What they ask for

  • Graduate degree in computer science, math, or a related field.
  • Experience building and delivering complex, real-time systems involving Agentic AI, Personalization, and Reinforced Learning.
  • Computer science fundamentals in data structures, algorithm design, and complexity analysis.
  • Track record of peer-reviewed scientific publications advancing applied science.

Nice to have

  • 15+ years of relevant, broad research experience after PhD degree or equivalent.
  • Experience taking projects from requirements and design through product launch; expertise in large-scale generative models, mentorship, and/or management is also preferred.

Pay

264,100.00 - 350,000.00 USD annually (base salary); 240,100.00 - 324,800.00 USD annually (base salary)

Tools

Agentic AI, Personalization, Reinforced Learning, large language models, GenAI, recommender systems

AI focus

Applied AI research for secure workplace systems, including multimodal LLMs and generative AI, agent collaboration and orchestration, personalization, recommender systems, and production deployment.

Model-written summary · the original posting is authoritative

First seen Oct 15, 2026 · Last verified Nov 5, 2026 (UTC)