Applied Science Manager , C360

Amazon
Palo Alto, CA, USA / Seattle, WA, USA2026-06-12ONSITE

About the job

We are working on improving shopping on Amazon using the conversational capabilities of large language models and through customer behavioral data to make them more personalized for each customer. 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. In this role, you will be managing a team working on Large Language Model (LLM) and/or Vision-Language Model (VLM) post-training and alignment for new shopping experiences. You’ll be working with talented scientists, engineers, and technical program managers (TPM) 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!

Responsibilities

- Managing a team working on Large Language Model (LLM) and/or Vision-Language Model (VLM) post-training and alignment for new shopping experiences.

- Working with talented scientists, engineers, and technical program managers (TPM) to innovate on behalf of customers.

Qualifications

Minimum

- 3+ years of scientists or machine learning engineers management experience

- Knowledge of ML, NLP, Information Retrieval and Analytics

- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field

Preferred

- Experience building machine learning models or developing algorithms for business application

- Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers

- 2+ years of scientists or machine learning engineers management experience

- Deep expertise with Large Language Model, Visual Language Models and Embedding modalities (vision and others). The candidate has published or has artifacts in working in the intersection of these models either during pre-training or post-training.