Do you want to build the backbone of Generative AI at AWS? Do you want to build the future of the cloud for AI training and inference, delivering continuous price performance improvements for multi-billion variable LLMs at cloud scale? Come join us. We are seeking a Systems Development Engineer to develop automation software, diagnostic tooling, and fleet health infrastructure for our accelerated (AI/ML) server platforms. You will work across multiple teams and organizations to build scalable, reliable systems that keep our fleet healthy — with a vision toward zero-touch operations where automation detects, diagnoses, and resolves issues without human intervention.
What You Will Do
You will solve complex architectural problems that may not be well-defined in advance. You will own your team's systems, proactively identify deficiencies, and write scalable, robust code to solve issues before they impact customers. You will decompose large, difficult server testability, reliability, and diagnosis problems into straightforward tasks and components — delivering yourself and through others in parallel — using a combination of hardware, software, system design, processor architecture, diagnostics, and operations knowledge.
Key job responsibilities
Fleet Health & Predictive Infrastructure
1. Build and own the automation infrastructure responsible for the health of the accelerator (AI/ML) compute server fleet
2. Design and implement predictive failure detection systems using telemetry, sensor data, error trending, and log correlation to identify hardware issues before they cause customer impact
3. Drive toward zero-touch operations — building automation that detects, diagnoses, triages, and remediates hardware and software faults without human intervention
4. Develop monitoring tools, dashboards, and alerting systems to provide real-time visibility into fleet health across lab and production environments
5. Define and track fleet health metrics (failure rates, mean time to detect, mean time to repair, first-time fix rate, predictive accuracy)
Debugging & Troubleshooting
1. Debug and resolve complex system-level issues across compute, GPU, and networking in production environments
2. Troubleshoot Linux boot and runtime failures across x86 and ARM architectures, including PCIe, power, NIC, NVMe, and GPU subsystems
3. Perform root cause analysis on hardware failures — correlating across firmware, kernel, driver, and physical layer to isolate faults
4. Build diagnostic tooling that automates root cause identification and reduces reliance on manual triage
5. Improve manufacturing throughput and yield through test optimization
Systems Development & Automation
1. Define and develop software, automation, and enabling tools for server hardware programs; track and report progress
2. Design and build scalable system-level software with focus on durability, availability, security, and diagnostics
3. Develop and maintain device drivers for Linux on ARM and x86 architectures
4. Build automation solutions using modern programming languages (Python, Ruby, Java, C/C++, etc.)
5. Work with OS internals and accelerator/GPU software stacks in Linux-based environments
6. Build, manage, and deploy CI/CD pipelines for rapid deployment of code changes to org-owned and customer-owned systems
Cross-Team Collaboration
1. Work across internal HWEng teams to ensure new server hardware addresses data path and control path functionality needed by dependent service teams
2. Work closely with internal customers to identify early any potential problems onboarding new accelerated compute servers into their ecosystem
3. Engage with ODMs and design partners on testability, diagnostic, and automation requirements during hardware design and development (NPI)
4. Contribute to server design to improve robustness, testability, diagnosability, and reliability
5. Partner with datacenter operations teams to close the loop between field failures and design improvements
A day in the life
You will collaborate with a variety of roles (SDEs, SDETs, Mechanical/Electrical/Hardware Engineers, TPMs, Managers, Principals) and organizations through server conception, test validation, qualification, launch, and operations — driving high quality and reliability into current and future designs for AWS accelerated server solutions. From orchestration tooling development to hardware integration to kernel driver debugging, you dive deep into problems across the breadth of AWS.
About the team
The Hardware Engineering AI/ML development team is a group of engineers and technical program managers directly responsible for launching and maintaining server hardware in the fleet — including AI/ML accelerator servers with GPUs. Located in Seattle, Cupertino, and Austin, we work with internal development teams, ODMs, and design partners to deliver servers deployed in datacenters worldwide.
