Senior Frontend Engineer - Machine Learning
- Seattle, WA
Are you intrigued or passionate about Machine Learning (ML) and ready to build a new customer-centric product? The Amazon SageMaker team is looking for talented front-end engineers to help us build the next generation of ML tools.
With SageMaker, developers and data scientists have the ability to build, train, and deploy machine learning models quickly. As a fully-managed cloud service, SageMaker covers the entire data science workflow from data preparation and exploratory data analysis to model building and inference; our charter is to make data science and machine learning understandable, affordable, scalable, and accessible to everyone. The foundation of the SageMaker user experience is the industry standard, open-source Jupyter Notebook.
As a front-end engineer on the SageMaker team, you'll be responsible for driving key deliverables within a new team building out interactive ML applications.
Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon's culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.]
Our team puts a high value on work-live balance. It isn't about how many hours you spend at home or at work; it's about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we're building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
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, visit US Disability Accommodations.
• Work closely with UX designers and product managers to develop friendly UI experiences.
• Work closely with other engineers to architect and develop the best technical design.
• Develop/maintain operational rigor for the frontend of a fast-growing AWS service.
• Own and deliver key features within a new interactive ML application
• Help develop the engineers of an existing "two pizza" scrum team.
• Collaborate with other SageMaker SDEs for features that cut across SageMaker.
• Engage with customers and other AWS partners.
• Help with hiring.
You'll be well supported with by a group with deep technical chops, including multiple senior and principal engineers and scientists.
• Bachelor's Degree in Computer Science or related field.
• Equivalent experience to a Bachelor's degree based on 3 years of work experience for every 1 year of education
• 5+ years professional experience in software development.
• Experience with modern programming languages (Java, C#, Python) and open-source technologies.
• Experience building tools for data scientists or developers.
• Attuned design sense so can collaborate with UX designers and hold a high bar with "backend" SDE's.
• Experience with with CI/CD in a frontend context.
• Experience establishing and leveraging web analytics.
• Machine learning knowledge and experience.
• 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.
• Ability to take a project from scoping requirements through actual launch of the project.
• Experience in communicating with users, other technical teams, and management to collect requirements, describe software product features, and technical designs.
• Deep hands-on technical expertise in full-stack development.
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