
Applied Scientist , Personalization & Ranking
Required skills
- machine learning
- recommendation systems
- large language models
- LLMs
- Deep learning
- Reinforcement Learning
- distillation
- Optimization methods
- A/B experiments
- data analyses
Nice to have
- Unix/Linux
- professional software development
Role summary
Amazon is seeking an Applied Scientist specializing in Personalization & Ranking to research, design, and develop state-of-the-art AI technologies. This role involves working with large datasets and computational resources to build and deploy machine learning models, including deep learning, LLMs, and reinforcement learning, for recommendation systems. The scientist will collaborate with engineers and product managers, conduct A/B experiments, and analyze data to optimize the customer shopping experience. A PhD or Master's degree with 3+ years of model-building experience and proficiency in Python, Java, or C++ is required.
As an Senior Applied Scientist in our team, you will be responsible for the research, design, and development of new AI technologies for Personalization. You will adopt or invent new machine learning and analytical techniques in the realm of recommendations and large language models. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include inventing, experimenting with, and launching new features, products and systems.
Key job responsibilities
- Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to optimize the shopping experience, and run these models directly on the retail website.
- Develop AI solutions for Recommendation systems using Deep learning, LLMs, Reinforcement Learning, distillation, and Optimization methods;
- Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end;
- Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses;
- Effectively communicate technical and non-technical ideas with teammates and stakeholders;
- Stay up-to-date with advancements and the latest modeling techniques in the field; Basic Qualifications: - 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field 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 Preferred Qualifications: - Experience using Unix/Linux
- Experience in professional software development
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
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