Description

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 build ML infrastructure to empower various customer shopping experiences, and develop shopping quality dashboard to continuously monitor and assess the quality of the personalized content on Amazon.com.

Amazon’s internal surveys regularly recognize us as one of the best 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.

Key job responsibilities

As a Sr. Applied Scientist on the team you will lead the roadmap and strategy for applying state-of-art ML technologies such as Multi-Modal, LLM, RAG and LLM Agent, and Computer Vision to build the next-generation ML systems to improve personalized shopping experience for our customers.

As a member of a highly leveraged team of talented engineers and ML scientists, you will have a unique opportunity to produce and deliver models that help build best-in-class customer experiences and build systems that allow us to deploy these models to production with low latency and high throughput.

We are building the next generation of ML systems that power the biggest personalized shopping engine on earth, and we hope you will join us!

About the team

Our mission is to delight every Amazon customer with a 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 a Senior Applied Scientist to build out adaptive and deeply personalized shopping experiences tailored to the customer's shopping journey.

We are open to hiring candidates to work out of one of the following locations:

New York, NY, USA | Seattle, WA, USA

Basic Qualifications

• 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

Preferred Qualifications

• 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 committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.