3+ years of non-internship professional software development experience
2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience working with PyTorch or JAX software
Post-Silicon Systems Validation Engineer, Annapurna Labs at Annapurna Labs (u.s.)
Bachelor's degree in computer science, engineering, mathematics or equivalent, or experience in Java, C++, Python, or a related language
3+ years of experience with hardware performance counters and profiling tools for analyzing and optimizing system and application performance
Strong understanding of computer architecture fundamentals including memory hierarchies (caches, DRAM, HBM), compute pipelines, and interconnect topologies
Experience applying statistical methods, regression analysis, and data visualization techniques to interpret performance data and drive optimization decisions
3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
Bachelor's degree in computer science or equivalent
Experience with Machine Learning Hardware/Software Architecture
Experience with CI/CD
Experience with EDA S
Additional Information
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
Annapurna Labs (u.s.) · Austin, TX