AmazonPosted 2mo ago

Senior Applied Scientist, Amazon Core Search

Senior Applied Scientist, Amazon Core Search at Amazon scores 97 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

IN, KA, Bengaluruseniorfull-time

AI in this role

tensorflowscikit-learn

nlp

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.

Basic qualifications

- PhD, or Master's degree
- 6+ years of building machine learning models or developing algorithms for business application experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning

Preferred qualifications

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

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Skills and AI tools this role asks for

NlpTensorFlowscikit-learn

Questions you could be asked

What NLP problem have you worked on, and how did you measure whether it actually worked?
Walk me through how you've used TensorFlow in your day-to-day work.
What are the limits of scikit-learn that you've run into, and how did you work around them?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

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List these exact terms on your resume: Nlp, TensorFlow, and scikit-learn. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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