Applied Scientist

- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience using Unix/Linux
- Experience in professional software development

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We are embarking on a multi-year journey to improve the shopping experience for customers globally. Amazon Search team creates customer-focused search solutions and technologies that make shopping delightful and effortless for our customers. Our goal is to understand what customers are looking for in whatever language happens to be their choice at the moment and help them find what they need in Amazon's vast catalog of billions of products — starting from the very first keystroke.

As Amazon expands to new interfaces, we are faced with the unique challenge of maintaining the bar on Search Results Quality and Search Autocomplete.

We are looking for a Applied Scientist II to work on improving search on Amazon using NLP, ML, and DL technology. As an Applied Scientist, you will lead our efforts in query understanding, semantic matching, and ranking. You will build systems that anticipate search query intent and surface the right results. As part of this role, you will develop high precision, high recall, and low latency solutions for search. Your solutions should work for all languages that Amazon supports and will be used in all Amazon locales world-wide. You will develop scalable science and engineering solutions that work successfully in production.

Key job responsibilities
As an Applied Scientist on the team, you will lead science innovation to improve the customer search experience through higher-quality search results. You will:
- Develop and deploy ML models to produce relevant search results.
- Design and train semantic matching models (bi-encoders, cross-encoders, and distillation from large foundation models) for ranking and relevance.
- Develop reinforcement learning and reward-modeling approaches to continuously improve search results quality.
- Train multi-objective ranking and scoring systems that balance suggestion diversity, specificity, and relevance.
- Design and implement scalable model architectures optimized for strict latency constraints, including knowledge distillation, quantization, and efficient inference strategies for production deployment.
- Lead end-to-end science projects from problem formulation through production launch, collaborating closely with engineers and scientists within and outside the team to deliver customer-facing impact.