DESCRIPTION How can we improve the shopping experience on Amazon.com by tailoring what we display on our pages based on customer interests and preferences? How do we use generative models to help us innovate in different ways to enhance the shopping experience? How do we generate personalized content that helps customers shop at scale?
Our team's stated missions is to "grow each customer's relationship with Amazon by leveraging our deep understanding of them to provide relevant and timely product, program, and content recommendations." Recommendations at Amazon is a way to help customers discover products. Our team strives to better understand how customers shop on Amazon (and elsewhere) and build recommendations models to streamline customers' shopping experience by showing the right products at the right time. Understanding the complexities of customers' shopping needs and helping them explore the depth and breadth of Amazon's catalog is a challenge we take on every day.
Using Amazon's large-scale computing resources, your team will build and deploy text and generation models that help customers shop on Amazon. You will ask research questions about customer behavior, develop state-of-the-art models that improve shopping features, and deploy these models to production. You will participate in the Amazon ML community and mentor Applied Scientists and software development engineers with a strong interest in and knowledge of ML and generative AI. Your team's work will directly benefit customers and the retail business.
We are looking for a passionate, hard-working, and talented Applied Science Manager who has experience building mission critical teams that design and deploy data-driven applications that customers love. You will have an opportunity to innovate and execute on an ambitious vision for generative AI and science initiatives that improve the everyday lives of millions of people.
We are open to hiring candidates to work out of one of the following locations:
Seattle, WA, USA BASIC QUALIFICATIONS - 3+ years of scientists or machine learning engineers management experience
- Knowledge of ML, NLP, Information Retrieval and Analytics
- Experience directly managing scientists or machine learning engineers
- Experience communicating with users, other technical teams, and management to collect requirements, describe software product features, and technical designs
- Experience with the Scrum methodology (or similar alternatives) for agile software development
- Experience in applied research
- Experience hiring and growing top talent
