Applied Scientist, AWS Marketplace & Partner Services

Amazon • Seattle, WA

6d • Onsite

About The Position

The AWS Marketplace team is looking for an Applied Scientist to help build and improve our AI/ML-Powered Discovery systems. The ideal candidate is passionate about applying machine learning and artificial intelligence to complex information retrieval challenges, with experience in search, recommendations, or personalization. You'll work alongside senior scientists and engineers to develop models and systems that power traditional discovery experiences — such as search ranking, query understanding, and personalized recommendations — built on rich product knowledge graphs and structured metadata. You'll also help extend these foundations into emerging agentic discovery experiences, where AI agents leverage these retrieval and knowledge systems to help customers find and evaluate software solutions. This is a great opportunity to have direct impact on the future of software discovery on AWS Marketplace.

Requirements

• 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 Haves

• 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

Responsibilities

• Design, develop, and deploy AI/ML models for search ranking, query understanding, recommendation, and discovery systems
• Develop and improve information retrieval systems that operate over complex product taxonomies, knowledge graphs, and structured/unstructured metadata
• Build relevance models that capture nuanced relationships between customer intent, product capabilities, and domain-specific context
• Advance both traditional discovery experiences (search, browse, personalized recommendations) and agentic discovery with multi-agent systems
• Conduct experiments and A/B tests to measure and improve the relevance and performance of discovery features
• Analyze large-scale datasets to identify patterns, generate insights, and inform model development
• Collaborate with engineers and product managers to integrate AI/ML solutions into production systems
• Stay current with relevant research in information retrieval, knowledge representation, NLP, and recommendation systems, and apply findings to improve our systems

Benefits

• health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
• 401(k) matching
• paid time off
• parental leave
• sign-on payments
• restricted stock units (RSUs)

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: Ph.D. or professional degree