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Senior Compiler Engineer III

2 months ago


Seattle, Washington, United States Annapurna Labs (U.S.) Inc. Full time
Company Overview:

Annapurna Labs, a division of AWS, is at the forefront of developing custom Machine Learning accelerators. We are seeking a skilled Senior Machine Learning Compiler Engineer III to enhance our Neuron Compiler team.


Product Insight:

Our team has pioneered AWS Machine Learning accelerators, including Inferentia and Trainium chips, designed for superior ML inference and training capabilities. These technologies are complemented by the AWS Neuron SDK, which encompasses a comprehensive ML compiler, runtime, and application framework.


Team Dynamics:

As a vital component of AWS, Annapurna Labs excels in silicon engineering, hardware design, software development, and operational excellence, delivering innovative solutions such as AWS Neuron and advanced ML accelerators.


Your Role:

In the capacity of a Senior Machine Learning Compiler Engineer III, you will play a crucial role in architecting and scaling a compiler tailored for extensive ML workloads. Your expertise in compiler technology and commitment to innovation will be essential.


Key Responsibilities:
  • Address intricate technical challenges across the software stack.
  • Design, implement, test, and deploy cutting-edge solutions to optimize service performance.
  • Develop high-quality, reliable products that enhance customer experiences.

About the Neuron Compiler Team:

Our team comprises a diverse group of engineers, researchers, and product specialists dedicated to crafting state-of-the-art compiler solutions for ML models derived from leading frameworks such as TensorFlow and PyTorch.


Essential Qualifications:
  • A minimum of 6 years of experience in system design and architecture leadership.
  • At least 5 years of experience in developing compiler features and optimizations.
  • Proficiency in programming languages such as C++, C, and Python.

Preferred Qualifications:
  • Advanced degree (Master's or PhD) in computer science.
  • Experience in optimizing deep learning models using TensorFlow, PyTorch, or JAX.
  • Familiarity with toolchains like LLVM, OpenXLA/XLA, MLIR, and TVM.