The Product: AWS Machine Learning accelerators are at the forefront of AWS innovation. The Inferentia chip delivers best-in-class ML inference performance at the lowest cost in cloud. Trainium delivers the best-in-class ML training performance with the most teraflops (TFLOPS) of compute power for ML in the cloud. This is all enabled by cutting edge software stack, the AWS Neuron Software Development Kit (SDK), which includes ML compiler, runtime and natively integrates into popular ML frameworks, such as PyTorch, TensorFlow and JAX. AWS Neuron along with the Inferentia/Trainium chips are used at scale with customers and partners both internal and external to Amazon.

The Team: The Amazon Annapurna Labs team is a responsible for building innovation in silicon and software for AWS customers. We are at the forefront of innovation by combining cloud scale with the world’s most talented engineers. Our team covers multiple disciplines including silicon engineering, hardware design and verification, software and operations. With such breadth of talent, there's opportunity to learn all of the time. 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. When you couple that with the ability to work on so many different products and services, it's a very unique learning culture.


Key job responsibilities
You: We are seeking a talented SW Engineering Manager with strong leadership/ mentoring skills to join our Deep Learning Compiler Team. As a Manager III you be building and leading a team of talented compiler engineers developing graph level optimizations to map SOTA deep learning models efficiently to our accelerator capabilities. You’ll leverage your technical skills to collaborate with ML applications teams and applied scientists developing the models to accelerate research ideas and techniques to bring them to production to faster serve our customers to their performance goals. You will partner with Pytorch, OpenXLA and other open source communities to leverage the work in both directions for the benefit of the machine learning community.

We are open to hiring candidates to work out of one of the following locations:

Cupertino, CA, USA