Interested in making an impact on the Machine Learning and AI ecosystem? As an SDE on the Amazon SageMaker StudioLab team, you'll own the onboarding website and data scientist IDE experience for AWS ML. Your team's mission is to provide a highly scalable and collaborative data science workbench where any data scientist, developer, or student can launch a wholly configured and collaborative workspace in the cloud. You can tryout the experience here https://studiolab.sagemaker.aws/ or watch the video to know more https://www.youtube.com/watch?v=k2nVIvHB1dk
Engineers on this team get to:
Engage with the maintainers of Jupyter/JupyterLab, etc. and make meaningful contributions to keep SageMaker on the cutting-edge.
Support online ML education providers (e.g, fast.ai, Udacity) and universities to develop the next generation of ML engineers.
Be very close to end customers as develop/combine open source software and examples to make a compelling customer experience.
Develop/maintain operational rigor for a fast-growing AWS service.
Work closely with principal engineers to architect and develop the best technical design.
Continually improve operational excellence.
Engage with open source and online ML education communities, staying abreast, influencing direction, and making contributions.
Collaborate with other SageMaker SDE's 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.
What is SageMaker?
Amazon SageMaker (https://aws.amazon.com/sagemaker/) is a fully-managed Machine Learning platform that makes it easy to build ML models, manage them, and integrate them with custom applications for batch or online predictions. SageMaker takes away the "heavy-lifting" normally associated with large-scale Machine Learning implementations so that developers and scientists can focus on the truly creative work of modeling and solving the business problem at hand.
What is SageMaker Studio Lab?
Amazon SageMaker Studio Lab is a free machine learning (ML) development environment that provides the compute, storage (up to 15GB), and security-all at no cost-for anyone to learn and experiment with ML. All you need to get started is a valid email address-you don't need to configure infrastructure or manage identity and access or even sign up for an AWS account. SageMaker Studio Lab accelerates model building through GitHub integration, and it comes preconfigured with the most popular ML tools, frameworks, and libraries to get you started immediately. SageMaker Studio Lab automatically saves your work so you don't need to restart in between sessions. It's as easy as closing your laptop and coming back later. Source - https://aws.amazon.com/sagemaker/studio-lab/
Inclusive Team Culture
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 16 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-life 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.
Mentorship & Career Growth
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.
4+ years of professional software development experience
3+ years of programming experience with at least one software programming language
2+ years of experience contributing to the system design or architecture (architecture, design patterns, reliability and scaling) of new and current systems
Experience as a mentor, tech lead OR leading an engineering team
Machine learning knowledge and experience.
Active engagement with an open source community.
Experience building tools for data scientists and developers.
Proficiency with notebook software (Jupyter, nteract, Zeppelin)
(Optional) Frontend development experience with React, TypeScript for web-based IDE innovation.
Experience 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.
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: large scale systems engineering and/or full-stack development.
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.