Applied Scientist, Grocery, Retail & In-store Experience (graise)

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

Applied Scientist role focused on designing, developing, and deploying machine learning and computer vision models for Amazon's in-store grocery technologies. The role involves end-to-end model development, from ideation to production, with a focus on solving complex grocery-domain problems at scale and improving the customer shopping experience.

What you'd actually do

  1. Design and implement machine learning models (computer vision, multi-modal learning, generative AI) to solve complex grocery-domain problems.
  2. Conduct exploratory data analysis and develop deep understanding of domain-specific data challenges.
  3. Collaborate with software engineers to productionize models and ensure reliability at scale.
  4. Define and track key metrics to evaluate model performance and business impact.
  5. Communicate findings and recommendations clearly to technical and non-technical stakeholders.

Skills

Required

  • PhD, or Master's degree and 2+ years of CS, CE, ML or related field experience
  • 2+ years of building models for business application experience
  • Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
  • Experience programming in Java, C++, Python or related language
  • Experience in designing experiments and statistical analysis of results
  • Experience in investigating, designing, prototyping, and delivering new and innovative system solutions

Nice to have

  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • Experience in professional software development

What the JD emphasized

  • productionize models
  • solve real-world problems at scale
  • computer vision
  • multi-modal learning
  • generative AI
  • deep learning models architecture design
  • deep learning training and optimization
  • model pruning

Other signals

  • design, develop, and deploy machine learning and computer vision models
  • productionize models
  • solve real-world problems at scale