Amazon.com-posted 3 months ago
$240,100 - $350,000/Yr
Full-time • Senior
New York, NY
5,001-10,000 employees
General Merchandise Retailers

At Secure Work Enablement (SWE), we're pioneering breakthrough AI technologies that are fundamentally transforming how millions of teams and businesses work. Our mission combines leading machine learning research with Amazon's unparalleled expertise in enterprise computing and security to create the next generation of intelligent workplace solutions. Our portfolio encompasses four innovative domains where applied AI research is critical: Next-Generation End User Computing, where we're developing novel machine learning models for human-AI collaboration; Amazon One's advanced biometric systems; Secure Collaboration platforms Wickr and Chime; and Gaia - our revolutionary AI-native workspace that's redefining human-AI agent interactions. Each area presents unique opportunities for advancing the state-of-the-art in machine learning, natural language processing, and AI systems. We're at an inflection point where traditional workplace computing is being revolutionized by AI technologies. Our distinctive challenge lies in solving complex machine learning problems at scale while maintaining strict security requirements - from developing sophisticated ML models for secure information access to creating intelligent systems that can understand and enhance human productivity in real-time. With an accomplished team of 650+ technologists and multiple tier-1 services, we're seeking a Senior Principal Scientist to spearhead our AI research and development initiatives. This role offers the opportunity to tackle unprecedented challenges in applied machine learning.

  • Developing novel AI architectures for secure, enterprise-grade collaborative systems
  • Advancing the science of human-AI interaction in workplace environments
  • Creating new frameworks for AI agent orchestration and optimization
  • Pioneering new approaches to privacy-preserving machine learning
  • Translating business and functional requirements into concrete deliverables
  • Inventing new product experiences that enable teams and agents to collaborate effectively
  • Liaising with internal Amazon partners and bringing state-of-the-art LLM/GenAI models to production
  • Defining a long-term science vision for the business and translating it into actionable plans
  • Working with academic partners to support in-house talent with access to leading research and mentoring
  • Graduate degree in Computer science/Math or related field
  • Experience in building complex, real-time systems involving Agentic AI, Personalization and Reinforced Learning
  • Demonstrated track record of project delivery for large, cross-functional projects with evolving requirements
  • Computer Science fundamentals in data structures, algorithm design and complexity analysis
  • Ability to develop machine learning platform strategy in the domain of recommender systems
  • Demonstrated track record of peer-reviewed scientific publications that advance state-of-the art for applied science
  • 15+ years of relevant, broad research experience after PhD degree or equivalent
  • Ability to take a project from requirements gathering and design to actual product launch
  • Exceptional customer understanding skills including the ability to discover the true challenges to efficient product discovery
  • Deep expertise in Machine Learning as applied to large-scale generative models
  • Proficiency in programming for algorithm and code reviews
  • Strong core competency in mathematics and statistics
  • Track record of successful projects in algorithm design and product development
  • Publications at top-tier peer-reviewed conferences or journals
  • Strong prior experience with mentorship and/or management of senior scientists and engineers
  • Thinks strategically, but stays on top of tactical execution
  • Exhibits excellent business judgment; balances business, product, and technology very well
  • Effective verbal and written communication skills with non-technical and technical audiences
  • Experience working with real-world data sets and building scalable models from big data
  • Base pay ranges from $240,100/year to $350,000/year depending on geographic market
  • Equity, sign-on payments, and other forms of compensation may be provided
  • Full range of medical, financial, and/or other benefits
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