Applied Scientist Ii, Genai Evaluation Media (gem)

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

Applied Scientist II role focused on GenAI Evaluation Media (GEM) for visual shopping experiences. The role involves research and development of agentic AI capabilities for multimodal understanding, visual content generation/editing, virtual try-on, and automated quality assurance. Success requires establishing robust metrics, collaborating cross-functionally, and delivering scalable solutions.

What you'd actually do

  1. Develop core science primitives for vision and language understanding, visual content generation and editing, virtual try-on, and automated quality assurance via state-of-the-art computer vision, machine learning, and generative AI
  2. Design and implement visual agentic systems, balancing visual quality, relevance, latency, and cost
  3. Define metrics and success criteria for your scientific initiatives, ensuring rigorous validation across customer touch points
  4. Own end-to-end delivery of research initiatives from problem formulation through experimentation to production deployment
  5. Stay current with latest advances in AI/ML and identify opportunities to apply them to your problem space

Skills

Required

  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • Experience in building models for business application
  • Experience programming in Java, C++, Python or related language

Nice to have

  • Experience in investigating, designing, prototyping, and delivering new and innovative system solutions
  • Experience in CS, CE, ML or related field research
  • Experience building machine learning models or developing algorithms for business application
  • Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms
  • Usage of generative AI tools to enhance workflow efficiency, with a willingness to learn effective prompting and evaluation practices.
  • Ability to recognize opportunities where generative AI could enhance products, workflows, or customer experiences.

What the JD emphasized

  • state-of-the-art computer vision, machine learning, and generative AI
  • visual agentic systems
  • automated quality assurance
  • customer touch points
  • production deployment
  • latest advances in AI/ML
  • agentic systems for visual content understanding and generation
  • customer value
  • science forums
  • customer-facing features
  • technical approaches
  • technical and non-technical stakeholders
  • building models for business application
  • deep learning algorithms
  • computer vision algorithms
  • generative AI tools
  • prompting and evaluation practices
  • generative AI could enhance products, workflows, or customer experiences

Other signals

  • building core science primitives for multimodal understanding
  • visual content generation and editing
  • personalized virtual try-on
  • automated quality assurance
  • agentic AI
  • multimodal personalization
  • real-time image/video generation