Amazon

ML Compiler Engineer , AWS Neuron, Annapurna Labs

Ontario, CA Toronto, Ontario
AWS Deep Learning TensorFlow PyTorch Machine Learning
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Description
- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience programming with at least one software programming language
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent

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, disability, age, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Do you love decomposing problems to develop products that impact millions of people around the world? Would you enjoy identifying, defining, and building software solutions that revolutionize how businesses operate?

The Annapurna Labs team at Amazon Web Services (AWS) is looking for a Software Development Engineer II to build, deliver, and maintain complex products that delight our customers and raise our performance bar. You’ll design fault-tolerant systems that run at massive scale as we continue to innovate best-in-class services and applications in the AWS Cloud.

Annapurna Labs was a startup company acquired by AWS in 2015, and is now fully integrated. If AWS is an infrastructure company, then think Annapurna Labs as the infrastructure provider of AWS. Our org covers multiple disciplines including silicon engineering, hardware design and verification, software, and operations. AWS Nitro, ENA, EFA, Graviton and F1 EC2 Instances, AWS Neuron, Inferentia and Trainium ML Accelerators, and in storage with scalable NVMe, are some of the products we have delivered, over the last few years.

At AWS our vision is to make deep learning pervasive for everyday developers and to democratize access to cutting edge infrastructure. In order to deliver on that vision, we’ve created innovative software and hardware solutions that make it possible.

AWS Neuron is the SDK that optimizes the performance of complex neural net models executed on AWS Inferentia and Trainium, our custom chips designed to accelerate deep-learning workloads

The Neuron SDK consists of a compiler, run-time, and debugger, integrated with Tensorflow, PyTorch, and MXNet. It’s preinstalled in AWS Deep Learning AMIs and Deep Learning Containers for customers to quickly get started with running high performance and cost-effective inference.

The Neuron team is hiring compiler engineers in order to solve our customers toughest problems.

This is an opportunity to work on cutting-edge products at the intersection of machine-learning, high-performance computing, and distributed architectures. You will architect and implement business-critical features, publish cutting-edge research, and mentor a brilliant team of experienced engineers. We operate in spaces that are very large, yet our teams remain small and agile. There is no blueprint. We're inventing. We're experimenting. It is a very unique learning culture.

As a deep learning compiler engineer on the Neuron team, you will be a thought leader supporting the development of a compiler targeting AWS Inferentia and Trainum. You will be developing and scaling the compiler to handle the world's largest ML workloads. You will need to be technically capable, credible and curious in your own right as a trusted AWS Neuron engineer, innovating on behalf of our customers. You will leverage your technical communications skill as a hands-on partner to AWS ML services teams and you will be involved in pre-silicon design, bringing new products/features to market, and many other exciting projects. A background in machine learning and AI accelerators is preferred, but not required.

Explore the product and our history!
https://awsdocs-neuron.readthedocs-hosted.com/en/latest/neuron-guide/neuron-cc/index.html

https://aws.amazon.com/machine-learning/neuron/

https://github.com/aws/aws-neuron-sdk

https://www.amazon.science/how-silicon-innovation-became-the-secret-sauce-behind-awss-success


Key job responsibilities
Our engineers collaborate across diverse teams, projects, and environments to have a firsthand impact on our global customer base. You’ll bring a passion for innovation, data, search, analytics, and distributed systems. You’ll also:

Solve challenging technical problems, often ones not solved before, at every layer of the stack.

Design, implement, test, deploy and maintain innovative software solutions to transform service performance, durability, cost, and security.Build high-quality, highly available, always-on products.

Research implementations that deliver the best possible experiences for customers.

A day in the life
As you design and code solutions to help our team drive efficiencies in software architecture, you’ll create metrics, implement automation and other improvements, and resolve the root cause of software defects. You’ll also:

Build high-impact solutions to deliver to our large customer base.

Participate in design discussions, code review, and communicate with internal and external stakeholders.

Work cross-functionally to help drive business decisions with your technical input.

Work in a startup-like development environment, where you’re always working on the most important stuff.

About the team
#1. Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

#2. Why AWS
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

#3. 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.

#4. 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.

#5. 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.

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