at Amazon in Chicago, Illinois, United States
Job Description
Description
Interested in helping build Prime’s Machine Learning system to drive huge business impact on millions of customers? Join our team of Scientists developing algorithms to adaptively generate and experiment on new content, personalize, and optimize the Prime membership experience. This includes identifying building foundational models that serve as an abstraction of our high-dimensional customer data, understanding who our customers are, and providing them with personalized experiences. As an ML lead, you will partner directly with product owners to intake, build, and directly apply your modeling solutions.
There are numerous scientific and technical challenges you will get to tackle in this role, such as deep learning techniques and natural language processing to abstract sequences and embeddings from customer features, offer/content features. These abstraction layers will then be used by our personalization, segmentation, and experimentation platforms. We employ techniques from deep learning, NLP, multi-armed bandits, optimization, and RL – while this role is focused on leading the cross-sectional space of deep learning, NLP, and RL.
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, Sagemaker, DynamoDB, S3, Andes, Bedrock …), various ML algorithms and techniques (Random Forests, Neural Networks, supervised/unsupervised/semi-supervised/reinforcement learning, LLM’s), 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 Deep Learning, NLP, and Reinforcement Learning for our 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, iden
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