Applied Scientist, Aws Neuron Science Team

Amazon Amazon · Big Tech · Santa Clara, CA · Applied Science

Applied Scientist role focused on enhancing AWS software stack for Trainium and Inferentia accelerators, involving ML/RL for kernel/code generation, ML compiler techniques, system robustness, and efficient kernel development. Collaborates with customers and engineering teams to optimize ML systems and adoption.

What you'd actually do

  1. Develop and apply ML/RL approaches for kernel/code generation and optimization
  2. Create advanced compiler techniques for ML workloads
  3. Build tools for accuracy and reliability validation
  4. Design high-performance kernels optimized for our ML accelerator architectures
  5. Work directly with external and internal customers to identify key adoption barriers and optimization opportunities

Skills

Required

  • PhD in computer science, computer engineering, or related field
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
  • Experience programming in Java, C++, Python or related language
  • Experience using Unix/Linux

Nice to have

  • Experience in investigating, designing, prototyping, and delivering new and innovative system solutions
  • Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
  • Experience with popular deep learning frameworks such as MxNet and Tensor Flow

What the JD emphasized

  • patents or publications at top-tier peer-reviewed conferences or journals

Other signals

  • ML/RL approaches for kernel/code generation and optimization
  • Machine Learning Compiler
  • System Robustness
  • Efficient Kernel Development
  • accelerating customer adoption of Trainium and Inferentia accelerators