Take Earth's most customer-centric company. Mix in hundreds of millions of shoppers spending tens of billions of dollars annually, an exciting opportunity to build next-generation shopping experiences, Amazon’s tremendous computational resources, and our extensive e-Commerce experience. What do you get? The most exciting position in the industry.
About our organization: Our team is part of Amazon’s Personalization organization, a high-performing group that leverages Amazon’s expertise in
machine learning, big data,
distributed systems, and user experience design to deliver the best shopping experiences for our customers. We run global experiments and our work has revolutionized e-commerce with features such as "Keep shopping for", "Tap to explore", “Customers who bought this item also bought”, and “Frequently bought together” among others. Amazon’s internal surveys regularly recognize us as one of the best engineering organizations to work for in the company, with visible high-impact work, low operational load, respectful work-life balance, and continual opportunity to learn and grow.
You will play a critical role in ideation for the team. We are building the next generation ML systems that powers the biggest shopping engine on earth, and we hope you will join us!
Key job responsibilities
As an Applied Scientist on the team you will be working on the state of art ways to help customers find the right products and content on their shopping journey. Our goal is to help customers achieve their objective seamlessly while shopping on Amazon.
We are investing in multiple fronts including but not limited to GenerativeAI, LLMs,
transformers, sequence models, reinforcement learning, MABs. This is an opportunity to come in on Day0 and influence the science roadmap of one of the most interesting problem spaces at Amazon - understanding the Amazon customer to build deeply personalized and adaptive shopping experiences. We will be working on applying state of the art science and research into production to elevate the customer experience. You will be part of a multidisciplinary team, working on one of the largest scale
machine learning systems in the company. You will hone your skills in areas such as deep learning and reinforcement learning while building scalable industrial systems. As a member of a highly leveraged team of talented engineers and ML scientists, you will have a unique opportunity to help build infrastructure that accesses petabytes of data to produce and deliver models that deliver state of the art customer experiences.
Key job responsibilities
As an Applied Scientist on the team you will be working on the state of art ways to help customers find the right products and content on their shopping journey. Our goal is to help customers achieve their objective seamlessly while shopping on Amazon.
We are investing in multiple fronts including but not limited to GenerativeAI, LLMs,
transformers, sequence models, reinforcement learning. This is an opportunity to come in on Day0 and influence the science roadmap of one of the most interesting problem spaces at Amazon - understanding the Amazon customer to build deeply personalized and adaptive shopping experiences. We will be working on applying state of the art science and research into production to elevate the customer experience. You will be part of a multidisciplinary team, working on one of the largest scale
machine learning systems in the company. You will hone your skills in areas such as deep learning and reinforcement learning while building scalable industrial systems. As a member of a highly leveraged team of talented engineers and ML scientists, you will have a unique opportunity to help build infrastructure that accesses petabytes of data to produce and deliver models that deliver state of the art customer experiences.
About the team
Our mission is to delight every Amazon customer with a consistent and adaptive personalized shopping experience. We achieve our mission through investments in large scale
machine learning,
distributed systems and user experience with the purpose of delivering the future of shopping on Amazon. We are seeking an Applied Scientist to work on step function science improvements to help achieve SOTA results and to help build new Personalization experiences for Amazon customers.
- 3+ years of building
machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in
Java,
C++,
Python or related language
- Experience with neural deep learning methods and
machine learning
- Experience with modeling tools such as R,
scikit-learn,
Spark MLLib, MxNet,
Tensorflow,
numpy,
scipy etc.
- Experience with large scale
distributed systems such as
Hadoop,
Spark etc.
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 - 167,100.00 - 226,100.00 USD annually