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Lead and conduct post-silicon validation for AI compute blades and racks, ensuring performance meets product standards while collaborating with multiple teams and mentoring engineers.

Lead post-silicon functional validation for AI SoCs: plan, develop and debug bare-metal tests on FPGA/emulator and first silicon, build Python automation and regression suites, analyse failures, collaborate across hardware, firmware and platform teams, and improve test infrastructure and methodologies.

Lead systems engineer for blade and rack validation of ARM/x86 server compute racks. Drive first-silicon bring-up, firmware integration, lab and debug tool development, test plan ownership, triage and resolution of HW/FW/SW issues, and cross-functional coordination to meet program milestones and improve validation capabilities.

Lead server and blade rack bring-up, install and configure servers, manage inventory via DCIM, run post-silicon validation and debug for CPU/GPU/HBM/IO, develop lab validation tools and scripts, coordinate data center projects and vendors, and drive technical improvements in system validation.

The Compliance Manager will develop and implement security frameworks for global lab environments, ensuring compliance, securing hardware, and managing access control.

Lead strategic sourcing for memory and storage technologies, manage supplier relationships, negotiate commercial agreements, develop cost models and risk mitigation plans, support NPI and production ramps, and collaborate cross-functionally to secure capacity and optimise cost, quality, and supply resilience for AI infrastructure platforms.

Lead global sourcing strategy for contract manufacturing partners: build and mentor a sourcing team, negotiate long-term agreements, develop cost models, ensure capacity and supply continuity, drive cross-functional alignment and supplier performance.

The Electrical Engineer will design hardware systems for AI applications, develop schematics, create PCBs, and collaborate on designs through production.

The Staff AI Performance Engineer will optimize performance across ARM-based architectures and distributed systems, analyzing AI workloads and collaborating to enhance system efficiency.

Design and optimize AI data center networks, focusing on high-performance computing and network fabrics, while collaborating with cross-functional teams.