
Applied Scientist, Perfect Order Experience (POE)
Required skills
- GenAI
- large language models
- foundation models
- tabular foundation model
- behavior foundation model
- product risk detection
- seller interactions capturing
- future seller behaviors prediction
- seller responses simulation
- Java
Nice to have
- model pruning
Role summary
Amazon is seeking a passionate Applied Scientist to join the Perfect Order Experience (POE) team. This role focuses on pioneering frontier science solutions to detect and treat product quality issues and enhance post-order experience. You will collaborate with scientists, engineers, and TPMs to drive innovation in GenAI foundation models, adapt LLMs to the domain, and develop efficient tabular and behavior foundation models. You will lead efforts to leverage Amazon's computing resources for GenAI and frontier ML advancements, aiming to improve seller and consumer outcomes within the Amazon ecosystem. This is an opportunity to shape the future of seller support.
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. Basic Qualifications: - 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 Preferred Qualifications: - PhD
- 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.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually
Sample Amazon interview questions
- 1
Implement Search a 2D Matrix
codingtechnicalmedium - 2
Implement LRU Cache
codingtechnicalmedium - 3
Implement Best Time to Buy and Sell Stock
codingtechnicaleasy - 4
Implement Flood Fill
codingtechnicaleasy - 5
Implement Top K Frequent Elements
codingtechnicalmedium
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