There are numerous scientific and technical challenges you will get to tackle in this role, such as deep learning and reinforcement learning, and their application to various types of contextual, multi-step optimization of the customer journey. We employ techniques from supervised learning, multi-armed bandits, optimization, and RL - while this role is focused on the space of discriminative and generative recommender systems.
As the central science team within Prime, our expertise gets routinely called upon to weigh in on a variety of topics. We also emphasize the need and value of scientific research and have developed a strong publication and patent record (internally/externally) which you will be a part of.
You will also utilize and be exposed to the latest in ML technologies and infrastructure: AWS technologies (EMR/Spark, Redshift, Sagemaker, DynamoDB, S3, ...), various ML algorithms and techniques (Random Forests, Neural Networks, supervised/unsupervised/semi-supervised/reinforcement learning, LLMs), and statistical modeling techniques.
Major responsibilities
- Build and develop machine learning models and supporting infrastructure at TB scale, in coordination with software engineering teams.
- Leverage Bandits, Supervised Learning, and Reinforcement Learning for Contextual Recommendation and Optimization Systems.
- Develop offline policy estimation tools and integrate with reporting systems.
- Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.
- Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes.
- Work closely with the business to understand their problem space, identify the opportunities and formulate the problems.
- Use machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems.
- Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems.
BASIC QUALIFICATIONS
- Experience programming in Java, C++, Python or related language
- PhD in computer science, mathematics, statistics, machine learning or equivalent quantitative field
- Experience applying theoretical models in an applied environment
PREFERRED QUALIFICATIONS
- Experience implementing algorithms using both toolkits and self-developed code
- Have publications at top-tier peer-reviewed conferences or journals
- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $129,400/year in our lowest geographic market up to $212,800/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.