Offers “Amazon”

39 days agoAmazon

Front End Engineer - Amazon AI

  • Seattle (King)
  • Teaching

Job description

DESCRIPTION

Interested in Machine Learning? As a Front End Engineering SDE on the AWS SageMaker Elastic Inference team, you’ll get to work on bringing UX experiences to ML model developers and operators to strategic and growing SageMaker services.

UX work includes working with these technologies:
1. SageMaker Management Console (https://console.aws.amazon.com/sagemaker): Expand Console support to develop unique experiences for SageMaker Elastic Inference. You work with SageMaker UX designers and deliver a console experience that enables customers to use the unique features of our services in conjunction with the larger end-to-end ML workflow of SageMaker.
2. Expand SageMaker Studio to develop unique experiences for Elastic Inference via Studio's AWS ML Notebook authoring and data scientist IDE experience. From a browser, Studio brings a scalable and collaborative data science workbench and sharable workspace in the cloud to SageMaker customers.
Key Responsibilities:
· Work closely with senior engineers, UX designers, and product managers to develop friendly UI experiences.
· Work closely with engineers to architect and develop the best technical design.
· Develop/maintain operational rigor for the frontend of a fast-growing AWS service.
· Collaborate with other SageMaker SDE's for Elastic Inference features that cut across SageMaker.
· Engage with customers and other AWS partners.
You'll be well supported with by a group with deep technical chops, including multiple senior and principal engineers.

What is SageMaker Elastic Inference?
SageMaker Elastic Inference (part of AWS AI) aims to deliver the best price performance on SageMaker by combining hardware acceleration using Elastic Inference accelerators (https://aws.amazon.com/machine-learning/elastic-inference) with software acceleration using the Neo compiler (https://aws.amazon.com/sagemaker/neo/), and tuned model servers. We not only power the inference experience on SageMaker, but also maintain and contribute to multiple open source projects.

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.

PREFERRED QUALIFICATIONS

· Experience building tools for data scientists or developers.
· Attuned design sense so can collaborate with UX designers and hold a high bar with service SDEs.
· Experience with with CI/CD in a front end 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.

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.

Desired profile

BASIC QUALIFICATIONS

- Master's degree in Engineering, Computer Science or related technical field, or equivalent experience
- Experience with web technologies (e.g., JavaScript/TypeScript, NodeJS, React, Angular)

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