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Applied Scientist, AWS Marketplace & Partner Services

Amazon - Seattle, Washington, USA

Posted May 4, 2026

Benefits

Parental leave
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Non-birth-parent leave
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Family-building benefits
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  • Adoption assistance: Not verified
  • Surrogacy assistance: Not verified
Mental health support
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Relocation assistance
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Childcare support
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Learning budget
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Verification
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Salary
$143K-$193K not verified - source not recorded; timestamp not recorded
401(k) match
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Market context

U.S. role benchmark (BLS OEWS)
$111,944 U.S. median for this role
Projected growth (BLS Employment Projections)
+13.7% - Much faster than average

50% above the BLS role benchmark for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

Shift type
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Weekend work
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Application

Cover letter
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Assessment
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Deadline
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Where they hire

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About this role

Applied Scientist, AWS Marketplace & Partner Services Seattle, Washington, USA 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. Key job 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 -

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