The Senior Validation Engineer role at Annapurna Labs focuses on validating next-generation machine learning accelerators that support AWS's cloud infrastructure. The position involves working in a dynamic environment with a team of experts to ensure high-quality performance across the entire product development lifecycle. Candidates will engage in various validation processes, from early design to post-silicon validation, collaborating with multiple teams to bridge hardware and software domains. This role is crucial for delivering exceptional results in AI/ML acceleration technology.

• Strong programming skills in languages such as Python, Lua, C/C++, Rust, or Go.
• A solid understanding of computer architecture and chip/system validation methodologies.
• Experience with cloud infrastructure and CI/CD processes.
• Firmware testing and/or development experience, including BIOS, BMC, and drivers.
• Domain expertise in areas such as PCIe, HBM, GPUs, neural networks, or ML hardware architecture.
• Knowledge of the full validation lifecycle from RTL simulation to silicon failure analysis and debug.
• 5+ years of programming experience with at least one software programming language.
• Bachelor's degree or above in computer science, computer engineering, or a related field.
• 5+ years of non-internship system test development experience.
• Experience with Linux environments and Git.
• Experience with server hardware and debug tools.
• Experience with Machine Learning Hardware/Software Architecture.
• Experience with EDA Simulations or Emulation.

• Develop comprehensive validation strategies and lead new methodologies to improve validation coverage.
• Create detailed test plans covering functional, performance, power, and stress testing.
• Mentor and provide direction to junior validation engineers.
• Execute complex test plans from RTL simulation and emulation environments through physical silicon validation.
• Handle complex debug including hands-on silicon bring-up and debug in the lab.
• Validate ML accelerator performance, accuracy, and reliability using real-world neural network workloads.
• Build test infrastructure, CI/CD, and automated regression frameworks for efficient validation.
• Collaborate across architecture, design, firmware, and software teams to triage failures.
• Review test results, identify patterns, and provide feedback to improve design quality.
• Deliver new tests to production systems in AWS data centers to enhance fleet health.

• Employees at Amazon are often offered comprehensive health benefits—including multiple medical plan options (no pre-existing condition exclusions, 100% covered in-network preventive care), dental and vision plans, a 24/7 medical advice line from day one, expert second-opinion services, and broad mental-health support with several free counseling sessions (including pediatric). Financial wellness typically includes a 401(k) with company match (up to 2%), Restricted Stock Units (equity), FSAs, an emergency savings program, product and partner discounts, and even college-savings and home-purchase programs. Overall, the package is designed to support employees and their families’ health, finances, and day-to-day life.