We’re building a foundational LLM for Amazon Stores that fuses general world knowledge with Amazon e-commerce domain knowledge to provide new and improved shopping experiences for our customers. We are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. You’ll be working with talented scientists, and engineers to innovate on behalf of our customers. If you’re fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey!
Key job responsibilities
In this role you will leverage your engineering background and expertise to help develop generative AI for shopping. On a day-to-day basis, you will:
- Develop big data and machine learning pipelines to enable the training, evaluation, and deployment of large language models.
- Create tooling to accelerate model experimentation and draw key insights from diverse aspects of model performance metrics
- Interface with partner engineering teams, applied scientists, and product managers to define requirements for production systems, collaborate on science experimentation, and creating plan for deployment.
- Move quickly and navigate ambiguity, make short-term and long-term trade offs, and deliver incrementally.
- Work on a wide range of technologies and problems, from big data distributed computing, internal tooling to streamline machine learning operations, to prompt engineering.
Key job responsibilities
In this role you will leverage your engineering background and expertise to help develop generative AI for shopping. On a day-to-day basis, you will:
- Develop big data and machine learning pipelines to enable the training, evaluation, and deployment of large language models.
- Create tooling to accelerate model experimentation and draw key insights from diverse aspects of model performance metrics
- Interface with partner engineering teams, applied scientists, and product managers to define requirements for production systems, collaborate on science experimentation, and creating plan for deployment.
- Move quickly and navigate ambiguity, make short-term and long-term trade offs, and deliver incrementally.
- Work on a wide range of technologies and problems, from big data distributed computing, internal tooling to streamline machine learning operations, to prompt engineering.