Our Alexa Product Advisor vision is to provide the best possible answers for a wide range of questions around product. Our customers ask various questions to Alexa regarding products, and not all the time we can find an answer in our knowledge sources. "Alexa, how strong is the magsafe on iPhone 12?" is a typical question that could be asked to our system. Our team works to build mechanism to understand the product utterance, find an answer and if no answer exists post the question to a community which could answer the same. We have machine learning models to understand the customer's request, then mechanism to go out seek and render the best possible answer to the customer. We think big, and any mechanism to go and search for a answer can be expanded to multiple use cases and knowledge sources, to bring the best experience for Alexa product answering. We are building various deep learning models to achieve the above goals. We also have a front end experience to enable customer to also search for the answers in our community. With a mix of back end, machine learning models and UX experience in the team, it provides a full stack experience for you to use your creativity to build an 'Amazon' experience!
If you are thinking how big is this, then think how we searched on desktops in 2000's, mobiles in 2010s and on voice and intelligent devices today! We want to provide a great experience though the intelligence we are building about products on any platform, making it easier for customers to learn about the products.
As an SDE on team, you will have significant influence on our overall strategy by helping define these product features, contribute and drive the system architecture, and enable the best practices that enable a quality product. If you have a flair for innovation and passion for solving some of the most challenging problems in the industry, you will love to take up the challenges we have on radar.
By submitting your application here, you can apply once to be considered for multiple Software Engineer openings across various Amazon teams. If you are successful in passing through the initial application review and assessment, you will be asked to submit your career and personal preferences so that our dedicated recruiters can match you to the right role based on these preferences.
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
· Master's degree in Computer Science or equivalent
· Experience taking a leading role in building complex software systems that have been successfully delivered to customers
· Knowledge of professional software engineering practices & best practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations
· Experience communicating with users, other technical teams, and senior management to collect requirements, describe software product features, technical designs, and product strategy
· Experience mentoring junior software engineers to improve their skills and make them more effective product software engineers
· Demonstrable track record of success in delivering new features and products.
· Demonstrated leadership abilities in an engineering environment in driving operational excellence and best practices.
· Highly quantitative with great judgment and passion for building a great customer experience.
· Programming experience with at least one modern language such as Java, C++, or C# including object-oriented design
· 1+ years of experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems.
· 2+ years of non-internship professional software development experience
· Good to have experience with science models deployment, build and measurements.
· Familiarity with machine learning basics could be helpful.