Post-silicon Systems Validation Engineer I, Annapurna Labs

Amazon Amazon · Big Tech · Austin, TX · Software Development

This role focuses on validating next-generation machine learning accelerators for AWS, covering the entire product development lifecycle from design to production. It involves working with hardware components, ML workloads, and collaborating with various engineering teams to ensure quality and performance of AI/ML accelerators.

What you'd actually do

  1. Developing comprehensive validation strategies and detailed test plans covering functional, performance, power, and stress testing from silicon bring-up to product release
  2. Executing complex test plans from RTL simulation and emulation environments through physical silicon validation
  3. Conducting hands-on silicon bring-up and debug in the lab using oscilloscopes, logic analyzers, and protocol analyzers
  4. Validating ML accelerator performance, accuracy, and reliability using real-world neural network workloads
  5. Building test infrastructure, CI/CD, and automated regression frameworks to enable efficient validation at scale

Skills

Required

  • Strong programming skills (Python, Lua, C/C++, Rust, Go, etc)
  • A solid understanding of computer architecture
  • Validation experience in any of these areas: PCIe, HBM, GPUs, neural networks, ML HW architecture, and/or CI/CD
  • Familiarity with the validation lifecycle from RTL simulation (SystemVerilog/UVM, VCS, Questa, Xcelium) and emulation (Palladium, Zebu, Veloce) through silicon failure analysis and debug
  • Currently has, or is in the process of obtaining a Bachelor’s or Master’s Degree in Computer Science, Computer Engineering, Data Science, Electrical Engineering, or majors relating to these fields
  • Strong programming skills in two or more of: C/C++, Rust, Go, Python, Lua
  • Familiarity with computer architecture (coursework or projects acceptable)
  • Experience with Linux environments and Git
  • Experience with system test development, code reviews, source control, build processes, or automated deployments

Nice to have

  • Experience with AWS services, cloud infrastructure, firmware development (BIOS, BMC, drivers)
  • Experience with AWS services or cloud infrastructure
  • Exposure to firmware development (BIOS, BMC, drivers)
  • Exposure to Machine Learning hardware or software architecture (coursework or projects acceptable)

What the JD emphasized

  • next-generation machine learning accelerators
  • AI training and inference
  • ML workloads
  • validation experience in any of these areas: PCIe, HBM, GPUs, neural networks, ML HW architecture, and/or CI/CD
  • familiarity with the validation lifecycle

Other signals

  • validating next-generation machine learning accelerators
  • power AWS's cloud computing infrastructure
  • AI training and inference
  • ML workloads