As an Applied Scientist on this team, you will build machine learning and deep learning models on large-scale global data to simulate customer behavior across the business. Ideally you bring experience with reinforcement learning, causal inference, and/or the design of agentic AI systems. You will partner closely with business, finance, engineering, and science stakeholders to take ideas from concept and prototype through to production. The candidate should have strong communication skills and the ability to translate data-driven findings into actionable insights. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail and the ability to work in a fast-paced and ever-changing environment.
Key job responsibilities
• Build models that simulate customer behavior to answer counterfactual "what-if" questions that guide major content and product investment decisions.
• Apply deep learning, reinforcement learning, causal inference, and experimental design to large-scale customer data to model how customers respond to change.
• Design and prototype agentic AI systems and other novel ML approaches and research new methods to improve the accuracy and scale of our models.
• Validate and calibrate models against real-world randomized experiments, and partner with software engineers to deliver scalable, production-ready systems.
• Translate model outputs into clear recommendations and communicate results to business, finance, and science stakeholders through both technical papers and business-facing documents.
BASIC QUALIFICATIONS
- 3+ years of building machine learning models or developing algorithms for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML 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
PREFERRED QUALIFICATIONS
- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
- Experience in investigating, designing, prototyping, and delivering new and innovative system solutions
- Experience in professional software development
- Experience with reinforcement learning, agentic AI system design and/or causal inference
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, CA, Culver City - 142,800.00 - 193,200.00 USD annually
USA, WA, SEATTLE - 142,800.00 - 193,200.00 USD annually
