Neuron Runtime Software Development Engineer , Neuron Runtime

Amazon Amazon · Big Tech · Cupertino, CA · Software Development

Software Development Engineer responsible for developing and maintaining high-performance runtime libraries and drivers for AWS Neuron AI accelerators. This includes working on the Neuron Runtime, profiler, ML Kernels, and ML Frameworks, with a focus on optimizing AI workloads and supporting frameworks like PyTorch, JAX, and XLA.

What you'd actually do

  1. develop and maintain high-performance runtime libraries and drivers for machine learning applications and AI accelerators
  2. work on design, development, and deployment of Neuron Runtime and other Neuron components
  3. Improving performance of ML Kernels and ML Frameworks
  4. manage the full development life cycle of the Neuron Runtime, ensuring scalability, reliability, and usability
  5. collaborate with cross-functional teams to ensure that the our C++ compiler generates key information so customers can understand and optimize the performance of our custom hardware

Skills

Required

  • software development experience
  • design or architecture of new and existing systems
  • programming with at least one software programming language

Nice to have

  • full software development life cycle
  • coding standards
  • code reviews
  • source control management
  • build processes
  • testing
  • operations
  • architecting, building, and operating distributed systems
  • high availability
  • fault tolerance
  • AWS services (e.g., EC2, ECS, CloudWatch, S3, Lambda) in production environments
  • Owning services end-to-end including deployment, monitoring, alarming, on-call, and post-incident review
  • PyTorch
  • JAX
  • XLA

What the JD emphasized

  • high-performance runtime libraries
  • AI accelerators
  • Neuron Runtime
  • profiler
  • optimizing AI workloads
  • ML Kernels
  • ML Frameworks
  • C++ compiler
  • PyTorch
  • JAX
  • XLA
  • architecting, building, and operating distributed systems
  • high availability
  • fault tolerance
  • AWS services
  • Owning services end-to-end
  • deployment
  • monitoring
  • alarming
  • on-call
  • post-incident review

Other signals

  • AWS Neuron
  • AI accelerators
  • runtime libraries
  • drivers
  • profiler
  • optimizing AI workloads
  • ML Kernels
  • ML Frameworks
  • C++ compiler
  • PyTorch
  • JAX
  • XLA