Senior Applied Scientist, Amazon Core Search

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 Assistance and Search Quality.

We are looking for a Senior Applied Scientist to work on improving search on Amazon using NLP, ML, and DL technology. As an Applied Scientist, you will lead our efforts in search autocomplete — developing high-quality suggestions. You will build systems that anticipate search query intent and surface the right suggestions and 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. You will work with leaders to develop a strategic vision and long term plans to improve search globally.

We are growing our collaborative group of engineers and applied scientists by expanding into new areas.

Key job responsibilities
As an Applied Scientist on the team, you will lead science innovation to improve the customer search experience through higher-quality autocomplete search results

• You will: - Develop and deploy ML models to produce high-quality, diverse search autocomplete suggestions.

• Design and train semantic matching models (bi-encoders, cross-encoders, and distillation from large foundation models) for suggestion ranking and relevance.

• Develop reinforcement learning and reward-modeling approaches to continuously improve suggestion 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, mentoring scientists and collaborating closely with engineers and scientists within and outside the team to deliver customer-facing impact.