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Description

We are seeking an Applied Scientist II to join our team in developing pioneering Generative AI, Agentic AI, Large Language Models (LLMs), and other advanced Machine Learning and Deep Learning solutions for Amazon Selection and Catalog Systems, within the Items and Relationships Intelligence Solutions team. This role offers a unique opportunity to work on AI products that will shape the future of online shopping experiences.

Our team operates at the forefront of AI research and development, working on challenges that directly impact millions of customers worldwide. We push the boundaries of AI at both the foundational and application layers. As an Applied Scientist II, you will have the chance to experiment with LLMs and deep learning techniques, apply your research to solve real-world problems at an unprecedented scale, and collaborate with experienced scientists to contribute to Amazon's scientific innovation.

Join us in redefining the future of shopping. Your work will directly influence how customers interact with the world's largest online store.

Key job responsibilities

- Design and implement novel AI solutions for Amazon catalog of products

- Develop and train state-of-the-art LLMs, Diffusion Models, and other Generative AI models

- Build and deploy autonomous AI Agents in Amazon production ecosystem

- Scale AI models to handle billions of diverse products across multiple languages and geographies

- Conduct research in areas such as Autonomous AI Agents, Generative AI, Language Modeling, Multi-modality Computer Vision, Diffusion Models, Reinforcement Learning

- Collaborate with cross-functional teams to integrate AI models into Amazon's production ecosystem

- Contribute to the scientific community through publications and conference presentations

Basic Qualifications

- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience

- 3+ years of building machine learning models for business application experience

- Experience programming in Java, C++, Python or related language

- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

- Experience in patents or publications at top-tier peer-reviewed conferences or journals

Preferred Qualifications

- Have publications on top-tier conferences, such as CVPR, ICCV, ECCV or NeurIPS

- Experience applying theoretical models in an applied environment

- Experience in designing experiments and statistical analysis of results

- Experience in investigating, designing, prototyping, and delivering new and innovative system solutions

- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $223,400/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.