Annapurna Labs designs silicon and software that accelerates innovation. Customers choose us to create cloud solutions that solve challenges that were unimaginable a short time ago—even yesterday. Our custom chips, accelerators, and software stacks enable us to take on technical challenges that have never been seen before, and deliver results that help our customers change the world.
We're looking to hire a Software Development Engineer that will:
- Develop mission-critical software that powers Annapurna Labs' next-generation machine learning platforms' interconnect (PCIe and Neuron Link)
- Collaborate with EC2 teams and manufacturing partners to ensure seamless system integration
- Drive end-to-end qualification processes for new hardware implementations
Technologies useful to this role include operating systems, Linux architecture, embedded systems, and control systems. Our team uses, C, C++, Lua, Bash, Python and other similar languages to develop device drivers, and develop automation software.
Key job responsibilities
As a member of the Annapurna Labs Machine Learning PCIe and Neuron Link engineering team, you will develop software to enable and monitor Annapurna accelerated compute servers and EC2 systems handling customer Machine Learning workloads in AWS Data Centers world wide. You will work closely with hardware engineers to bring up new boards, custom silicon devices, and servers for EC2 accelerated computing instances. You will provide inputs to architects on the development of custom silicon and system features. You will develop automated software test and deployment pipelines to ensure software quality, compatibility, and upgradeability.
A day in the life
Daily tasks range from A to Z - as long as it relates to a PCIe interface, we're on it. This includes programming on device interfaces using standard subsystems such as I2C and SPI, as well as working on software which integrates the server with EC2, for diagnostics and performance.
About the team
The Annapurna Labs Machine Learning PCIe and Neuron Link team is responsible for all aspects of the interconnect between accelerators for the custom Trainium AI servers.
Our team consists of hardware, software, and firmware engineers all working together to create innovative and scaleable solutions for the next-generation of Trainium AI servers.
- 3+ years of non-internship professional software development experience
- 3+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Bachelor's degree in computer science or equivalent
- Experience with PCIe subsystems or controllers. Experience can range from supporting PCIe devices to programming controller firmware to device driver implementation.
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, 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.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits
USA, TX, Austin - 143,700.00 - 194,400.00 USD annually
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