Post-Silicon Systems Validation Engineer, Annapurna Labs

Amazon.com Inc

Austin, TX

JOB DETAILS
SKILLS
Amazon Web Services (AWS), Architectural Design, Artificial Intelligence (AI), C Programming Language, C++ Programming Language, Cloud Computing, Computer Architecture, Computer Firmware, Computer Programming, Continuous Deployment/Delivery, Continuous Integration, Debugging Skills, Design Verification, Failure Analysis, Functional Testing, GPU (Graphics Processing Unit), Go Programming Language (Golang), Hardware Architecture, Hardware Components, Hardware Design, Infrastructure as a Service (IaaS), Internet Technology, Logic Analyzer, Lua, Machine Learning, Network Operations Center, Neural Networks, Oscilloscope, PCI Express (PCI-E), Printed Circuit Board (PCB), Problem Solving Skills, Product Development, Product Lifecycle, Production Support, Production Systems, Protocol Analysis, Python Programming/Scripting Language, Quality Management, RTL Design, RTL Verification, Root Cause Analysis, Rust Programming Language, Silicon Bringup, Simulation, Startup, Strategic Planning, Stress Testing, System Validation, SystemVerilog, Systems Engineering, Technical Operations, Test Plan/Schedule, Testing
LOCATION
Austin, TX
POSTED
29 days ago

Annapurna Labs, an AWS organization with development centers in the U.S. and Israel, builds custom silicon and software for AWS customers. Our team combines cloud-scale innovation with world-class expertise across silicon engineering, hardware design, verification, software, and operations to tackle technical challenges that have never been seen before.

Join our Silicon Validation team to validate next-generation machine learning accelerators that power AWS"s cloud computing infrastructure. You"ll work in a fast-paced, startup-like environment alongside some of the brightest minds in the industry on cutting-edge, internet-scale technology that directly impacts how customers use Machine Learning acceleration. We are changing the landscape of cloud infrastructure by accelerating the development of custom silicon by moving beyond traditional partnerships to dominate in AI training and inference

Your work will span validation of the complete vertical stack-silicon, PCB, high-speed components (HBM, PCIe, chip-to-chip), inter-system connections, and system-to-system interfaces. You"ll dive deep into new technology hardware components and scaling technologies that power our Machine Learning boards and servers at scale, ensuring every component of our hardware and software comes together into products our customers rely on.

Key job responsibilities

As a Validation Engineer on our Machine Learning Acceleration team, you"ll own critical validation aspects across the entire product development lifecycle-from early design validation through emulation, silicon bring-up, post-silicon validation, and ongoing support of production systems deployed in AWS data centers. You"ll collaborate deeply with architecture, RTL design, design verification, firmware, and software teams to ensure our next-generation AI/ML accelerators meet the highest standards of quality and performance. This role requires bridging multiple domains-from low-level hardware interfaces to high-level ML workloads-to deliver exceptional results.

We are looking for candidates with:

  • Strong programming skills (Python, Lua, C/C++, Rust, Go, etc)
  • A solid understanding of computer architecture
  • Experience with AWS services, cloud infrastructure, firmware development (BIOS, BMC, drivers)
  • 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

A day in the life

  • Developing comprehensive validation strategies and detailed test plans covering functional, performance, power, and stress testing from silicon bring-up to product release
  • Executing complex test plans from RTL simulation and emulation environments through physical silicon validation
  • Conducting hands-on silicon bring-up and debug in the lab using oscilloscopes, logic analyzers, and protocol analyzers
  • Validating ML accelerator performance, accuracy, and reliability using real-world neural network workloads
  • Building test infrastructure, CI/CD, and automated regression frameworks to enable efficient validation at scale
  • Collaborating across architecture, design, firmware, and software teams to triage failures and drive root cause analysis to closure
  • Reviewing test results, identifying patterns, and providing feedback to improve design quality and validation coverage
  • Supporting production systems in AWS data centers and addressing field issues as they arise

About the Company

A

Amazon.com Inc

At Amazon, we don’t wait for the next big idea to present itself. We envision the shape of impossible things and then we boldly make them reality. So far, this mindset has helped us achieve some incredible things. Let’s build new systems, challenge the status quo, and design the world we want to live in. We believe the work you do here will be the best work of your life.

Wherever you are in your career exploration, Amazon likely has an opportunity for you. Our research scientists and engineers shape the future of natural language understanding with Alexa. Fulfillment center associates around the globe send customer orders from our warehouses to doorsteps. Product managers set feature requirements, strategy, and marketing messages for brand new customer experiences. And as we grow, we’ll add jobs that haven’t been invented yet.

It’s Always Day 1
At Amazon, it’s always “Day 1.” Now, what does this mean and why does it matter? It means that our approach remains the same as it was on Amazon’s very first day – to make smart, fast decisions, stay nimble, invent, and stay focused on delighting our customers. In our 2016 shareholder letter, Amazon CEO Jeff Bezos shared his thoughts on how to keep up a Day 1 company mindset. “Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight,” he wrote. “A customer-obsessed culture best creates the conditions where all of that can happen.” You can read the full letter here

Our Leadership Principles
Our Leadership Principles help us keep a Day 1 mentality. They aren’t just a pretty inspirational wall hanging. Amazonians use them, every day, whether they’re discussing ideas for new projects, deciding on the best solution for a customer’s problem, or interviewing candidates. To read through our Leadership Principles from Customer Obsession to Bias for Action, visit https://www.amazon.jobs/principles
COMPANY SIZE
10,000 employees or more
INDUSTRY
Retail
FOUNDED
1994
WEBSITE
http://Amazon.com/militaryroles