Applied Scientist Ii, Genai Evaluation Media (gem)

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

Applied Scientist II focused on GenAI Evaluation Media (GEM) for visual shopping experiences. The role involves research and development of agentic AI capabilities for visual understanding, content generation, personalization (virtual try-on), and automated quality assurance. It emphasizes multimodal understanding, real-time generation, and scalable personalization, integrating computer vision, NLP, and generative AI to create agentic shopping experiences. Success requires defining metrics, cross-functional collaboration, and staying at the forefront of AI advancements. The role requires rigorous research and practical engineering skills for production deployment.

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

  • visual agentic experiences
  • multi-modal personalization
  • real-time image/video generation
  • multimodal understanding
  • visual content generation and editing
  • personalized virtual try-on
  • automated quality assurance
  • multimodal conversation
  • visual responses
  • accurate, real-time visual understanding and generation
  • contextual understanding
  • scalable personalization
  • agentic AI
  • style goals
  • computer vision
  • natural language processing
  • generative AI
  • human-centered design
  • agentic shopping experiences
  • human specialist
  • domain knowledge base
  • robust metrics
  • cross-functional partners
  • asset effectiveness
  • customer touch points
  • rapid advances in AI technology
  • deep technical expertise
  • Computer Vision
  • Generative AI
  • scientific work
  • customer and business outcomes
  • scientists
  • engineers
  • stakeholders
  • innovation
  • scientific excellence
  • customer obsession
  • rigorous research skills
  • practical engineering instincts
  • solutions that scale
  • applied AI research
  • systems
  • customers discover and evaluate products and styles
  • visual experiences
  • core science primitives
  • vision and language understanding
  • visual content generation and editing
  • virtual try-on
  • automated quality assurance
  • state-of-the-art computer vision
  • machine learning
  • generative AI
  • visual agentic systems
  • visual quality
  • relevance
  • latency
  • cost
  • metrics and success criteria
  • scientific initiatives
  • rigorous validation
  • customer touch points
  • end-to-end delivery
  • research initiatives
  • problem formulation
  • experimentation
  • production deployment
  • latest advances in AI/ML
  • opportunities to apply them
  • problem space
  • development and deployment
  • scalable agentic systems
  • visual content understanding and generation
  • high scientific and engineering standards
  • complex technical problems
  • practical focus on customer value
  • team's culture of scientific excellence
  • presentations and publications
  • internal and external science forums
  • product and engineering teams
  • customer-facing features
  • scientists and engineers
  • multiple teams within Amazon
  • technical approaches
  • research findings
  • technical trade-offs
  • technical and non-technical stakeholders
  • PhD
  • Master's degree
  • CS
  • CE
  • ML
  • related field experience
  • building models for business application
  • Java
  • C++
  • Python
  • related language
  • investigating
  • designing
  • prototyping
  • delivering new and innovative system solutions
  • CS
  • CE
  • ML
  • related field research
  • building machine learning models
  • developing algorithms for business application
  • developing and implementing deep learning algorithms
  • computer vision algorithms
  • generative AI tools
  • workflow efficiency
  • prompting and evaluation practices
  • opportunities where generative AI could enhance products
  • workflows
  • customer experiences

Other signals

  • visual agentic experiences
  • multi-modal personalization
  • real-time image/video generation
  • customer shopping
  • visuals
  • multimodal understanding
  • visual content generation and editing
  • personalized virtual try-on
  • automated quality assurance
  • multimodal conversation
  • visual responses
  • accurate, real-time visual understanding and generation
  • contextual understanding
  • scalable personalization
  • agentic AI
  • style goals
  • computer vision
  • natural language processing
  • generative AI
  • human-centered design
  • agentic shopping experiences
  • human specialist
  • domain knowledge base
  • robust metrics
  • cross-functional partners
  • asset effectiveness
  • customer touch points
  • rapid advances in AI technology
  • deep technical expertise
  • Computer Vision
  • Generative AI
  • scientific work
  • customer and business outcomes
  • scientists
  • engineers
  • stakeholders
  • innovation
  • scientific excellence
  • customer obsession
  • rigorous research skills
  • practical engineering instincts
  • solutions that scale
  • applied AI research
  • systems
  • customers discover and evaluate products and styles
  • visual experiences
  • core science primitives
  • vision and language understanding
  • visual content generation and editing
  • virtual try-on
  • automated quality assurance
  • state-of-the-art computer vision
  • machine learning
  • generative AI
  • visual agentic systems
  • visual quality
  • relevance
  • latency
  • cost
  • metrics and success criteria
  • scientific initiatives
  • rigorous validation
  • customer touch points
  • end-to-end delivery
  • research initiatives
  • problem formulation
  • experimentation
  • production deployment
  • latest advances in AI/ML
  • opportunities to apply them
  • problem space
  • development and deployment
  • scalable agentic systems
  • visual content understanding and generation
  • high scientific and engineering standards
  • complex technical problems
  • practical focus on customer value
  • team's culture of scientific excellence
  • presentations and publications
  • internal and external science forums
  • product and engineering teams
  • customer-facing features
  • scientists and engineers
  • multiple teams within Amazon
  • technical approaches
  • research findings
  • technical trade-offs
  • technical and non-technical stakeholders
  • PhD
  • Master's degree
  • CS
  • CE
  • ML
  • related field experience
  • building models for business application
  • Java
  • C++
  • Python
  • related language
  • investigating
  • designing
  • prototyping
  • delivering new and innovative system solutions
  • CS
  • CE
  • ML
  • related field research
  • building machine learning models
  • developing algorithms for business application
  • developing and implementing deep learning algorithms
  • computer vision algorithms
  • generative AI tools
  • workflow efficiency
  • prompting and evaluation practices
  • opportunities where generative AI could enhance products
  • workflows
  • customer experiences