The Books Personalization team helps customers to find the next book they would love. Our systems and algorithms operate on one of the world's largest book catalogs, recommending books to customers - with a strict latency constraint. We are a team of software engineers working alongside machine learning scientists on complex solutions to understand the customer intent and present them with books are relevant to their liking.
We are looking for a Software Engineer, who can drive appropriate technology choices for the business, lead the way for continuous innovation, and shape the future of recommendation systems in Amazon Books. You will build services to handle billions of requests per day, while maintaining response latencies in milliseconds and meeting strict SLA requirements. It is quite routine for our systems to operate on massive datasets using distributed frameworks. You will design and code, troubleshoot, and support high volume and low latency distributed systems. You will develop ML models either from scratch or by modifying existing algorithms. The solutions you create would drive step increases in building better recommender and/or helping more customers. You will directly impact our customers’ book shopping experience. This role will provide exposure to cutting-edge innovations in recommender system, as well as working experience on science side of the spectrum.
About the team
The Recommendations Xperiences (REX) "Core Recs" team is an engineering-focused team working in the Books Personalization domain. Together, we build novel services, infrastructures, and Machine Learning (ML) models to enhance the book personalization experience for customers.
BASIC QUALIFICATIONS
- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience programming with at least one software programming language
PREFERRED QUALIFICATIONS
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
- Experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems
- Experience building complex software systems that have been successfully delivered to customers
- 1+ years of building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization or search experience
- Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence
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 $129,300/year in our lowest geographic market up to $223,600/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.