AI HW Systems Engineering and Debug Lead
Location: Austin, Texas, United States
Department: HW Platform Dev.
About us
Graphcore is one of the world’s leading innovators in Artificial Intelligence compute. It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry.
As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone.
Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore enjoys a culture of continuous learning and constant innovation.
Job Summary
We are seeking an experienced AI HW Systems Engineering and Debug Lead to drive system-level debug and bring-up activities for Graphcore’s next-generation AI data center platforms.
The successful candidate will lead complex debug efforts across hardware, firmware, and software layers for blade and rack-level systems. This role focuses on developing scalable debug strategies, improving debug throughput, and ensuring timely resolution of system-level issues throughout the product lifecycle.
The Team
Graphcore is a globally recognised leader in Artificial Intelligence computing systems. The company designs advanced semiconductors and data centre hardware that provide the specialised processing power needed to drive AI innovation, while delivering the efficiency required to support its broader adoption
The Systems Engineering and Validation team ensures Graphcore’s AI compute platforms are fully validated, debugged, and ready for deployment in hyperscale data center environments.
The team collaborates closely with silicon engineering, system architecture, firmware, operating system, and rack integration teams to identify and resolve system-level issues and drive improvements in validation and debug methodologies.
Responsibilities and Duties
- Own and develop AI systems debug methodology and system bring-up strategies for next-generation AI data center platforms.
- Lead system-level debug and root cause analysis for issues identified during server rack validation, post-silicon validation, and production phases.
- Drive complex debug efforts across silicon, hardware platforms, firmware, operating systems, and software stacks.
- Manage and track technical issues, risks, and priorities to ensure program milestones are achieved.
- Publish debug program indicators and metrics to identify roadblocks and improve debug throughput.
- Coordinate cross-functional teams including system architecture, silicon, firmware, and validation teams to resolve system-level issues.
- Lead development and integration of debug tools, scripts, and methodologies to improve debug efficiency.
- Communicate program status, risks, and technical findings to engineering leadership and stakeholders.
Candidate Profile
Essential
- Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, or related discipline.
- 15+ years of experience working on complex systems engineering challenges involving HW/FW/SW debug in server or data center environments.
- Proven experience leading validation and debug for board, blade, and rack-level hardware platforms.
- Strong experience debugging OS, firmware, silicon, and hardware issues.
- Understanding of industry-standard system buses such as PCIe and CXL and their software stacks.
- Strong knowledge of ARM or x86 CPU architectures, SoC design, memory systems, and power management.
- Experience with system architecture, validation strategies, and complex system debug methodologies.
- Strong collaboration, communication, and cross-team coordination skills.
Desirable
- Experience designing or deploying AI/ML rack-scale systems.
- Experience developing at-scale debug methodologies for hyperscale data center systems.
- Familiarity with data center infrastructure and emerging AI hardware technologies.
- Experience with rack integration testing and hyperscale deployment readiness.
- Knowledge of automated validation frameworks, test analytics, and continuous validation practices.
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