We are seeking a talented applied researcher to join the Whole Page Planning and Optimization (WPPO) Science team in Search. The latest data from Business Insider shows that almost 50% of online shoppers visit Amazon first. The Search WPPO Science team is responsible for developing large-scale machine learning systems—spanning ranking, reinforcement learning, and large language models (LLMs)—that power the next generation Amazon shopping experience and deliver it to millions of customers. We believe that shopping on Amazon should be simple, delightful, and full of WOW moments for EVERYONE, whether you are technically savvy or new to online shopping.

Key job responsibilities
As an Applied Scientist, you will work closely with a team of applied scientists and engineers to build systems that shape the future of Amazon's shopping experience by generating relevant content with LLMs and assembling a whole page experience that is coherent, dynamic, and interesting. You will improve our ranking and optimization algorithms. You will participate in driving features from idea to deployment, and your work will directly impact millions of customers.

You are going to love this job because you will:

* Apply state-of-the-art Machine Learning (ML) algorithms, including Deep Learning, Reinforcement Learning, and Large Language Models (LLMs), to improve hundreds of millions of customers' shopping experience.
* Have measurable business impact using A/B testing.
* Work in a dynamic team that provides continuous opportunities for learning and growth.
* Work with leaders in the field of machine learning.

Basic qualifications

• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
• Experience programming in Java, C++, Python or related language
• 1+ years of building machine learning models or developing algorithms for business application experience
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred qualifications

• 1+ years of building production software experience
• Ph.D. in computer science, mathematics, statistics, machine learning or equivalent quantitative field
• Experience in written and verbal communication with the ability to present complex technical information in a clear and concise manner to executives and non-technical leaders
• At least 2 years of experience with predictive modeling and analysis, applying various machine learning techniques including supervised/unsupervised learning, deep learning, and reinforcement learning
• Strong publication record at top ML conferences and journals

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

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.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, CA, Palo Alto - 171,600.00 - 222,200.00 USD annually
USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually