Amazon's Perfect Order Experience (POE) AI team is focused on building scalable scientific solutions to ensure flawless customer experiences and protect customers from abuse and infringement, while empowering sellers and brands globally.
Requirements
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- 3+ years of building models for business application experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- 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 applying theoretical models in an applied environment
- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
Responsibilities
- Design and develop advanced AI systems using LLMs and machine learning for infringement and abuse prevention at scale.
- Build automated AI solutions that emulate human decision-making capabilities.
- Conduct data analysis and translate insights into practical ML solutions.
- Collaborate with cross-functional teams to implement and scale solutions.
- Own end-to-end solution development from conception to production deployment.
Other
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- 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.
- Our compensation reflects the cost of labor across several US geographic markets.
