Senior System Tests & Diagnostics Engineer

Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical discipline.

6+ years of experience developing hardware diagnostics, system validation software, firmware validation tools, or low-level systems software.

Strong software development skills in Python and C/C++.

Strong Linux systems experience.

Strong understanding of modern server platform architecture, including how CPU, memory, PCIe, storage, networking, firmware, operating system, and device drivers interact within a complete system.

Experience debugging hardware/software interactions across multiple layers of the platform.

Experience developing diagnostics, stress testing, or hardware validation software.

Experience with server platforms, embedded systems, or SoC validation.

Strong analytical and debugging skills.

Experience collaborating across hardware, firmware, software, and validation teams.

Excellent communication and problem-solving skills.

Preferred Qualifications Arm-based server platforms AI accelerators or high-performance computing systems Silent Data Corruption (SDC) testing Hardware stress testing Power transient analysis Hardware fault injection methodologies Firmware validation Silicon bring-up Performance characterization Hardware telemetry and observability BMC firmware or server management RAS (Reliability, Availability, Serviceability) technologies Validate across subsystems: CPU scaling and cache behavior Memory (DDR/HBM) bandwidth, latency, and NUMA effects Interconnect contention under multi-core load PCIe/I-O throughput, latency, and multi-device scenarios High-speed I/O validation What Success Looks Like Successful candidates will: Extend existing diagnostics technologies to support new Graphcore hardware. Develop new diagnostics tools for emerging hardware capabilities. Build reusable software components that integrate seamlessly with the existing validation framework. Improve hardware observability and root-cause isolation. Detect and characterize intermittent hardware failures that traditional validation techniques cannot expose. Deliver configurable diagnostics that scale across multiple platforms and silicon revisions. Work effectively across hardware, firmware, validation, and diagnostics teams to continuously improve platform quality.

Arm-based server platforms

AI accelerators or high-performance computing systems

Silent Data Corruption (SDC) testing

Hardware stress testing

Power transient analysis

Hardware fault injection methodologies

Firmware validation

Silicon bring-up

Performance characterization

Hardware telemetry and observability

BMC firmware or server management

RAS (Reliability, Availability, Serviceability) technologies

Validate across subsystems:

CPU scaling and cache behavior

Memory (DDR/HBM) bandwidth, latency, and NUMA effects

Interconnect contention under multi-core load

PCIe/I-O throughput, latency, and multi-device scenarios

High-speed I/O validation

Extend existing diagnostics technologies to support new Graphcore hardware.

Develop new diagnostics tools for emerging hardware capabilities.

Build reusable software components that integrate seamlessly with the existing validation framework.

Improve hardware observability and root-cause isolation.

Detect and characterize intermittent hardware failures that traditional validation techniques cannot expose.

Deliver configurable diagnostics that scale across multiple platforms and silicon revisions.

Work effectively across hardware, firmware, validation, and diagnostics teams to continuously improve platform quality.

This role provides a unique opportunity to work at the intersection of hardware architecture, diagnostics, and software engineering while helping define how next-generation AI systems are validated.

Rather than maintaining an existing validation framework, you will develop the specialized diagnostics and stress tools that make that framework valuable. Your work will directly influence hardware quality, silicon bring-up, and long-term platform reliability for future AI infrastructure.