Applied Scientist, Perfect Order Experience (POE)
# Applied Scientist, Perfect Order Experience (POE) **Amazon.com Services LLC** · Seattle, Washington, USA · `On-site` · `full-time` 🕒 **Статус:** *Опубликовано: вчера* · *Источник: Amazon* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[Applied Science]` `[Applied Science]` --- ### About the Role We’re pioneering frontier science solutions to detect and treat product quality issues and enhance post-order experience in Amazon. Our goal is to enhance support for our diverse seller community and foster improved outcomes for both sellers and consumers for broader Amazon ecosystem. We are looking for passionate innovators who are excited about technology, driven by customer experience, and eager to make a lasting impact on the industry. In this role, you'll collaborate with top-tier scientists, engineers, and technical program managers (TPMs) to drive innovation in GenAI foundation models, adapt Large language model to our domain, develop efficient tabular foundation model, innovate on behavior foundation model. You will lead the effort to leverage Amazon's large-scale computing resources to accelerate advances in GenAI and frontier ML solutions. If you’re enthusiastic about joining a dynamic and motivated team, this is your chance to be part of an exciting journey. Apply now and help us shape the future of seller support at Amazon! Key job responsibilities 1. Develop domain-specific foundation models. 2. Apply the domain-specific foundation model to product risk detection, seller interactions capturing, future seller behaviors prediction, and seller responses simulation across varied conditions. 3. Work with business and engineers to develop and deploy the solutions. - 3+ years of building models for business application experience - PhD, or Master's degree and 4+ years of science, technology, engineering or related field experience - Experience in patents or publications at top-tier peer-reviewed conferences or journals - Experience programming in Java, C++, Python or related language - Experience applying theoretical models in an applied environment
- Applied Science
Наблюдалась 2026-09-16, впервые 2026-09-16, источник — Amazon.