Senior Applied Scientist

Amazon Amazon · Big Tech · Seattle, WA · Machine Learning Science

Senior Applied Scientist at Amazon focused on using Generative AI, VLMs, and multimodal reasoning to understand product identity and relationships within Amazon's catalog. The role involves formulating research problems, designing and implementing models for product relationship inference and catalog understanding, pioneering explainable AI, owning ML pipelines from research to production, defining research roadmaps, and mentoring peers. It emphasizes tackling ambiguous problems at scale, reasoning across text and images, and deploying solutions that impact millions of customers.

What you'd actually do

  1. Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval—translating ambiguous business challenges into tractable scientific frameworks
  2. Design and implement leading models leveraging VLMs, foundation models, and agentic architectures to solve product identity, relationship inference, and catalog understanding at billion-product scale
  3. Pioneer explainable AI methodologies that balance model performance with scalability requirements for production systems impacting millions of daily customer decisions
  4. Own end-to-end ML pipelines from research ideation to production deployment—processing petabytes of multimodal data with rigorous evaluation frameworks
  5. Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements

Skills

Required

  • applied research experience
  • building machine learning models for business application experience
  • PhD or Master's degree
  • programming in Java, C++, Python or related language
  • neural deep learning methods
  • machine learning

Nice to have

  • training and deploying machine learning systems to solve large-scale optimizations
  • LLMs, foundation models, or large-scale deep learning systems
  • Publications in top-tier venues
  • Visual Language Models (VLMs), multimodal transformers, or vision-language pretraining
  • explainable AI, model interpretability, or uncertainty quantification
  • Strong experimental design skills
  • statistical analysis expertise
  • Generative AI, including prompt engineering, fine-tuning, RLHF, or agentic architectures

What the JD emphasized

  • 4+ years of applied research experience
  • 3+ years of building machine learning models for business application experience
  • PhD, or Master's degree and 6+ years of applied research experience
  • Experience with neural deep learning methods and machine learning
  • Experience with training and deploying machine learning systems to solve large-scale optimizations
  • Prior experience in the domains of LLMs, foundation models, or large-scale deep learning systems
  • Publications in top-tier venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, EMNLP, ACL, NAACL, COLING, KDD, SIGMOD, WWW, AAAI, or similar
  • Experience with Visual Language Models (VLMs), multimodal transformers, or vision-language pretraining
  • Experience with explainable AI, model interpretability, or uncertainty quantification
  • Strong experimental design skills and statistical analysis expertise
  • Hands-on experience with Generative AI, including prompt engineering, fine-tuning, RLHF, or agentic architectures

Other signals

  • Generative AI
  • Visual Language Models
  • multimodal reasoning
  • agentic shopping experiences
  • large-scale information retrieval
  • product identity, relationship inference, and catalog understanding
  • explainable AI methodologies
  • end-to-end ML pipelines
  • foundational research
  • incremental product improvements
  • GenAI and multimodal AI