Applied Scientist, Regulatory, Intelligence, Safety and Compliance (risc)

Amazon Amazon · Big Tech · Seattle, WA · Applied Science

Applied Scientist role focused on agentic AI, GenAI, and Machine Learning for regulatory compliance at Amazon. The role involves designing and evaluating state-of-the-art algorithms for content generation, multi-modal classification, intent detection, information retrieval, anomaly detection, and agentic systems. It requires developing and deploying ML models at scale, with an emphasis on scientific innovation and publication.

What you'd actually do

  1. Design and evaluate state-of-the-art algorithms and approaches in content generation, multi-modal classification, global product taxonomy, intent detection, information retrieval, anomaly and fraud detection, agentic AI, generative AI and multi-agent system.
  2. Translate product and CX requirements into measurable science problems and metrics.
  3. Collaborate with product and tech partners and customers to validate hypothesis, drive adoption, and increase business impact
  4. Key author in writing high quality scientific papers in internal and external peer-reviewed conferences.

Skills

Required

  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • Experience programming in Java, C++, Python or related language
  • Experience building machine learning models or developing algorithms for business application
  • Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals

Nice to have

  • Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects)
  • Experience in professional software development

What the JD emphasized

  • state-of-the-art large-language-models (LLMs)
  • multi-modal model
  • agentic AI
  • generative AI
  • multi-agent system
  • state-of-the-art deep learning models architecture design
  • deep learning training and optimization
  • model pruning
  • patents or publications at top-tier peer-reviewed conferences or journals

Other signals

  • agentic AI
  • GenAI
  • LLMs
  • multi-modal model
  • production systems at Amazon-scale