Applied Scientist, Amazon Search
Amazon - Bengaluru, Karnataka, IND
Posted Apr 1, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
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- Mental health support
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- Relocation assistance
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- Childcare support
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Schedule
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- Weekend work
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Application
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- Deadline
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Where they hire
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About this role
Applied Scientist, Amazon Search Bengaluru, Karnataka, IND At Amazon Search, we discover experience that connects millions of customers to the products they seek across our global marketplace. Our team develops sophisticated search solutions that make Amazon's vast product catalog easily accessible to customers worldwide. We're building next-generation search infrastructure that enables seamless expansion into new markets and product categories. Our mission is to ensure every customer enjoys an exceptional search experience from day one, whether they're shopping in an established market or a newly launched region. We develop intelligent, scalable systems that optimize search quality and effectiveness across Amazon's diverse product ecosystem. Our innovations help customers find exactly what they're looking for, while enabling Amazon to rapidly expand its global presence with consistent, high-quality search capabilities. We are seeking a strong applied scientists to join the Search Relevance India team. This team's charter is to increase the pace at which Amazon expands and improve the search experience at launch. In practice, we aim to invent universally applicable signals and algorithms for training machine-learned ranking models and improve the machine-learning framework for training and offline evaluation that is used for all new relevance models. Key job responsibilities * Build machine learning models for Product Search. * Develop new ranking features and techniques building upon the latest results from the academic research community. * Propose and validate hypothesis to direct our business and product road map. Work with engineers to make low latency model predictions and scale the throughput of the system.
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