Applied Scientist, Aws Marketplace & Partner Services

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

Applied Scientist at AWS Marketplace focused on building and improving AI/ML-powered discovery systems. The role involves developing models for search ranking, query understanding, and recommendations, and extending these into agentic discovery experiences using multi-agent systems. Collaboration with engineers and product managers to deploy solutions into production is key.

What you'd actually do

  1. Design, develop, and deploy AI/ML models for search ranking, query understanding, recommendation, and discovery systems
  2. Develop and improve information retrieval systems that operate over complex product taxonomies, knowledge graphs, and structured/unstructured metadata
  3. Build relevance models that capture nuanced relationships between customer intent, product capabilities, and domain-specific context
  4. Advance both traditional discovery experiences (search, browse, personalized recommendations) and agentic discovery with multi-agent systems
  5. Conduct experiments and A/B tests to measure and improve the relevance and performance of discovery features

Skills

Required

  • Experience programming in Java, C++, Python or related language
  • PhD in computer science, machine learning, engineering, or related fields
  • 3+ years of building machine learning models or developing algorithms for business application experience

Nice to have

  • Experience with knowledge graphs, entity resolution, or taxonomy-based retrieval systems
  • Experience with learning-to-rank, semantic search, or neural information retrieval techniques
  • Experience with deep learning frameworks (PyTorch, TensorFlow) and large-scale data processing (Spark, etc.)
  • Familiarity with large language models, retrieval-augmented generation, or agentic AI systems
  • Experience deploying AI/ML models to production at scale
  • Familiarity with NLP techniques, embeddings, or transformer-based models
  • Track record of published research or patents in relevant areas

What the JD emphasized

  • building machine learning models or developing algorithms for business application experience
  • deploying AI/ML models to production at scale

Other signals

  • AI/ML-Powered Discovery systems
  • search ranking, query understanding, and personalized recommendations
  • agentic discovery experiences
  • multi-agent systems