SysDE1 AWS SageMaker Notebooks, AI/ML Amazon Dedicated Cloud

Seattle, Washington, USA

Applications have closed

Amazon.com

Free shipping on millions of items. Get the best of Shopping and Entertainment with Prime. Enjoy low prices and great deals on the largest selection of everyday essentials and other products, including fashion, home, beauty, electronics, Alexa...

View company page

Job summary
Interested in making an impact on the Machine Learning and AI ecosystem? As a SysDE on the Amazon SageMaker Notebooks team, you’ll own the core platform (control plane and data plane) for various interactive applications (e.g. JupyterLab). Our team's mission is to enable any interactive application to scale reliably and securely so any data scientist, developer, or student can launch a wholly configured and collaborative workspace in the cloud. You will work in the company of world experts and there are immense learning opportunities.

Engineers on this team get to:
  • Develop in multiple layers of the stack including distributed workflows, high throughput data planes, linux networking, and system security.
  • Build fundamental primitives in the cloud for enabling data scientists/data engineers workflows.
  • Develop/maintain operational rigor for a fast-growing AWS service.

A successful engineer joining the team will do much more than write code and triage problems. They will work with Amazon's largest and most demanding customers to address specific needs across a full suite of services. They will dive deeply into technical issues and work diligently to improve the customer experience. The ideal candidate will:
* Be great fun to work with. Our company credo is "Work hard. Have fun. Make history". The right candidate will love what they do and instinctively know how to make work fun.
* Have strong Linux & Networking Fundamentals. The ideal candidate will have deep experience working with Linux, preferably in a large scale, distributed environment. You understand networking technology and how servers and networks inter-relate. You regularly take part in deep-dive troubleshooting and conduct technical post-mortem discussions to identify the root cause of complex issues.
* Love to code. Whether its building tools in Java or solving complex system problems in Python, the ideal candidate will love using technology to solve problems. You have a solid understanding of software development methodology and know how to use the right tool for the right job.
* Think Big. The ideal candidate will build and deploy solutions across thousands of devices. You will strive to improve and streamline processes to allow for work on a massive scale.

Note that this is a backend engineering position.

Key Responsibilities:
  • Assist in gathering and analyzing business and functional requirements, and translate requirements into technical specifications for robust, scalable, supportable solutions that work well within the overall system architecture.
  • Engage with customers and other AWS partners
  • Serve as technical lead on complex projects using best practice engineering standards, and hire/mentor junior development engineers
  • 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 Notebooks?

An Amazon SageMaker notebook instance is a machine learning (ML) compute instance running the Jupyter Notebook App. SageMaker manages creating the instance and related resources. Use Jupyter notebooks in your notebook instance to prepare and process data, write code to train models, deploy models to SageMaker hosting, and test or validate your models. Read more at https://docs.aws.amazon.com/sagemaker/latest/dg/nbi.html

About Us

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 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.

Work/Life Balance
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.

About the team
About Us
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.

Work/Life Balance
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. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded professional and enable them to take on more complex tasks in the future.

Basic Qualifications


  • Bachelor's Degree in Computer Science or Engineering
  • 2+ years of software development experience
  • Computer Science fundamentals in object-oriented design, data structures and algorithms.
  • Proficiency in, at least, one modern programming language such as C++, Java, or Python
  • Working knowledge of Linux system administration
  • Experience of systems automation using BASH, Python, Perl, etc

Preferred Qualifications

  • Solid understanding of Linux performance tuning and problem diagnosis
  • 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.
  • Experience of monitoring frameworks (such as CloudWatch, Datadog, Grafana, Elastic or similar)


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.

Tags: Architecture AWS Computer Science Engineering Grafana Jupyter Linux Machine Learning ML models Perl Python SageMaker Security Testing

Perks/benefits: Career development Conferences Startup environment

Region: North America
Country: United States
Job stats:  1  0  0

More jobs like this

Explore more AI, ML, Data Science career opportunities

Find even more open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general - ordered by popularity of job title or skills, toolset and products used - below.