Sr. Component Engineer, Annapurna Labs, Machine Learning Hardware

Amazon

Amazon

Software Engineering, Other Engineering
Austin, TX, USA
Posted on Sep 27, 2025

Description

Annapurna Labs (our organization within AWS UC) 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 are looking for a Component Engineer with strong skills and background in hardware design and firmware support. In this role you will work with supply chain team, help with component selection, optimize cost and be responsible for system validation and integration of hardware in the AWS fleet through its entire life cycle.

You will work cross functionally with the Platform Design team, and additional teams across AWS to improve quality and reliability of products operating in the fleet.

We are looking for candidates who thrive in a fast-paced start-up like environment and work independently to deliver multiple projects in parallel. To be successful you need to be highly motivated and detailed oriented while meeting the highest standards and time to market, cost and quality goals.

About the team
In 2015, Annapurna Labs was acquired by Amazon Web Services (AWS). Since then, we have accelerated its innovation and developed a number of products that benefit cloud customers, including AWS Nitro technology, Inferentia custom Machine Learning chips, and AWS Graviton2 processors.

Annapurna Labs is a silicon/system and software organization that is delivering all the chips used by AWS customers. Today this includes: Graviton, driving innovation for general purpose compute; Nitro, driving networking and storage scale, security and Hypervisor offload, and Machine Learning (ML) Trainium and Inferentia that are enabling customers to train and run GenAI applications permanently while keeping costs under control.