Senior Deep Learning Compiler Engineer
- Seattle, WA
At AWS AI 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 an SDK that optimizes the performance of complex neural net models executed on AWS Inferentia, our first fully custom chip 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 hiring senior compiler engineers with a background in machine learning and AI accelerators in order to solve our customers toughest problems.
As a senior deep learning compiler engineer on The Neuron team, you will be a thought leader supporting the ground-up development of a compiler targeting AWS Inferentia. The team is comprised of some of the brightest minds in the engineering, research, and product community, focused on the ambitious goal of creating a toolchain that will provide a quantum leap in performance.
This is an opportunity to work on a brand new product where you will architect and implement business-critical features, publish cutting-edge research, and mentor a brilliant team of experienced engineers. Join us today and shape the future of one of AWS fastest growing products!
• B.S. Computer Science or related technical field
• 5+ years of professional experience in software engineering
• Proficiency with one or more of the following programming languages: C, C++, Java, or Perl
• 5+ years of experience architecting and optimizing LLVM compilers
• M.S. or Ph.D. in Computer Science or related technical field
• Experience with multiple toolchains, compilers, and Instruction Set Architectures
• Strong knowledge of resource management, scheduling, code generation, and compute graph optimization
• Meets/exceeds Amazon's leadership principles requirements for this
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