About the job
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.
Responsibilities
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.
Qualifications
Minimum
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
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