Sr Systems Development Engineer, AWS Hardware Engineering Services, AI UltraServers

AWS runs the world's largest fleet of AI/ML accelerator servers. When a model with billions of parameters trains across a large scale of GPUs, every minute of downtime costs real progress. We are building the automation, diagnostics, and predictive intelligence that keeps this fleet running at peak. If you want to work at the intersection of hardware, software, and scale — where your code directly prevents customer-impacting failures — this is the role. We are seeking a Systems Development Engineer to build automation software, diagnostic tooling, and fleet health infrastructure for our accelerated compute platforms. You will work across multiple teams and organizations to design scalable, reliable systems for our accelerated compute fleet.

You will tackle problems no one has fully defined yet — spanning hardware, firmware, kernel, and software simultaneously. You will own systems end to end, writing code that prevents failures rather than reacts to them, and building automation that replaces manual toil with intelligent self-healing. You will work across PCIe topology, GPU diagnostics, Linux drivers, and telemetry pipelines to correlate signals and isolate faults at fleet scale. When your system catches a failing GPU before a training job crashes, that is your impact. Why You Will Love It Your automation runs at a large scale across servers in the cloud. When you ship, you see failure rates move within days. The team is small enough that your decisions shape the architecture, and large enough that you will always have experts to learn from across hardware, firmware, and software. The Ideal Candidate You know the full stack from bare-metal to userland. You debug at the intersection of components, not just within them. You build at cloud scale and care how your systems decisions impact customers. You are an excellent communicator who can drive alignment across hardware, software, and operations teams. Key job responsibilities Fleet Health & Predictive Infrastructure 1. Build and own the automation infrastructure for accelerator (AI/ML) fleet health at a large scale of servers, driving toward zero-touch operations that detect, diagnose, triage, and remediate faults without human intervention 2. Design and develop test frameworks, test coverage strategies, and diagnostic tooling to validate hardware functionality, detect faults, and ensure qualification coverage across the platform lifecycle. 3. Design predictive failure detection using telemetry, sensor data, error trending, and log correlation to identify degrading components before customer impact 4. Develop monitoring dashboards and alerting for real-time fleet health visibility across manufacturing, lab, and production environments 5. Define and track fleet health metrics: failure rates, mean time to detect and resolve issues, first-time fix rate, test dwell time, and predictive accuracy Debugging & Troubleshooting 1. Debug complex system-level issues across compute, GPU, and networking in production — including Linux boot/runtime failures, PCIe, power, NIC, NVMe, and GPU subsystems on x86 and ARM 2. Perform root cause analysis correlating across firmware, kernel, driver, and physical layer; feed findings into manufacturing quality and design improvements Systems Development & Automation 1. Design scalable test automation for hardware bring-up, regression, and qualification — reducing manufacturing test cycle times without sacrificing coverage through intelligent test sequencing and parallel execution 2. Build data pipelines correlating test results, sensor telemetry, and component-level data to identify systemic yield issues and drive upstream fixes 3. Develop and maintain Linux device drivers on ARM and x86; work with OS internals and accelerator/GPU software stacks 4. Build and manage tests covering all functional aspects of the system and CI/CD pipelines for rapid deployment to manufacturing lines and production fleet Cross-Team Collaboration