Applied Scientist, Aws Applied AI Solutions Core Services

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

Applied Scientist role focused on developing and productizing AI solutions for enterprise services within AWS. The role involves designing and implementing ML systems for diverse applications, creating scalable algorithms, conducting experimentation with advanced techniques (LLMs, CV, agentic AI), and collaborating with engineering and product teams to deliver customer impact. The primary output is shipped AI products, with a secondary focus on agentic AI systems.

What you'd actually do

  1. Develop and productize AI solutions that address complex technical challenges requiring novel approaches beyond off-the-shelf tools
  2. Design and implement machine learning systems for diverse applications including video understanding, geospatial optimization, fraud detection, anomaly detection, and automation
  3. Create scalable algorithms and models that generalize across multiple customer use cases and business problems
  4. Conduct rigorous experimentation with state-of-the-art techniques including large language models, computer vision, federated learning, or physics-based modeling, and agentic AI systems
  5. Collaborate with engineering teams to integrate science components into production systems with measurable customer impact

Skills

Required

  • building models for business application experience
  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • Experience programming in Java, C++, Python or related language
  • Experience in algorithms and data structures
  • Experience in parsing
  • Experience in numerical optimization
  • Experience in data mining
  • Experience in parallel and distributed computing
  • Experience in high-performance computing

Nice to have

  • Experience using Unix/Linux
  • Experience in professional software development

What the JD emphasized

  • novel approaches beyond off-the-shelf tools
  • scalable algorithms and models
  • measurable customer impact
  • novel approaches beyond off-the-shelf solutions
  • scalable production solutions
  • building models for business application experience
  • publications at top-tier peer-reviewed conferences or journals

Other signals

  • Develop and productize AI solutions
  • Design and implement machine learning systems
  • Create scalable algorithms and models
  • Conduct rigorous experimentation with state-of-the-art techniques
  • Integrate science components into production systems