Systems Development Engineer, AWS Generative AI & ML Servers

About the role

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.

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.

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.

Responsibilities

• Solve complex architectural problems that may not be well-defined in advance.
• Own your team's systems, proactively identify deficiencies, and write scalable, robust code to solve issues before they impact customers.
• 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.
• Build and own the automation infrastructure responsible for the health of the accelerator (AI/ML) compute server fleet.
• 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.
• Drive toward zero-touch operations — building automation that detects, diagnoses, triages, and remediates hardware and software faults without human intervention.
• Develop monitoring tools, dashboards, and alerting systems to provide real-time visibility into fleet health across lab and production environments.
• Define and track fleet health metrics (failure rates, mean time to detect, mean time to repair, first-time fix rate, predictive accuracy).
• Debug and resolve complex system-level issues across compute, GPU, and networking in production environments.
• Troubleshoot Linux boot and runtime failures across x86 and ARM architectures, including PCIe, power, NIC, NVMe, and GPU subsystems.
• Perform root cause analysis on hardware failures — correlating across firmware, kernel, driver, and physical layer to isolate faults.
• Build diagnostic tooling that automates root cause identification and reduces reliance on manual triage.
• Improve manufacturing throughput and yield through test optimization.
• Define and develop software, automation, and enabling tools for server hardware programs; track and report progress.
• Design and build scalable system-level software with focus on durability, availability, security, and diagnostics.
• Develop and maintain device drivers for Linux on ARM and x86 architectures.
• Build automation solutions using modern programming languages (Python, Ruby, Java, C/C++, etc.).
• Work with OS internals and accelerator/GPU software stacks in