Principal Engineer - GPU Software Architect

AMD is a company focused on building innovative products that enhance next-generation computing experiences. As a GPU Software Architect, you will provide technical leadership in GPU architecture and software enablement while ensuring the successful integration of software libraries with hardware platforms.

Responsibilities:

• Provide technical leadership for GPU architecture decisions with direct impact on multi‑ASIC platforms, interconnects, memory systems, and scalability
• Translate architectural concepts into concrete platform requirements spanning ASIC, firmware, drivers, and software libraries
• Define and lead bring‑up strategies for new GPU platforms, including strategies spanning multiple ASICs
• Partner with silicon, systems, and software teams to identify risks early and drive mitigation plans from pre‑silicon through first silicon
• Drive hardware/software interface definition, ensuring architecture choices support and reflect the drive towards performance and quality
• Influence firmware, driver, runtime, and performance software design to align with architectural intent
• Act as a technical escalation point during early silicon bring‑up, debugging complex cross‑layer issues spanning hardware, firmware, and software
• Guide the creation of diagnostics, validation tools, and bring‑up workflows that scale across teams and products
• Work across architecture, design, verification, drivers, performance libraries, and product teams to ensure alignment
• Provide technical mentorship and review, raising the overall effectiveness of teams working on new GPU platforms
• Capture lessons learned from new product bring‑up and translate them into reusable architecture patterns, best practices, and documentation
• Leverages AI‑assisted software development tools to accelerate the design, implementation, review, and documentation of complex software libraries
• Establishes best practices for responsible use of AI assistance, including validation, review, and traceability of generated code and technical artifacts

Requirements:

• Provide technical leadership for GPU architecture decisions with direct impact on multi-ASIC platforms, interconnects, memory systems, and scalability
• Translate architectural concepts into concrete platform requirements spanning ASIC, firmware, drivers, and software libraries
• Define and lead bring-up strategies for new GPU platforms, including strategies spanning multiple ASICs
• Partner with silicon, systems, and software teams to identify risks early and drive mitigation plans from pre-silicon through first silicon
• Drive hardware/software interface definition, ensuring architecture choices support and reflect the drive towards performance and quality
• Influence firmware, driver, runtime, and performance software design to align with architectural intent
• Act as a technical escalation point during early silicon bring-up, debugging complex cross-layer issues spanning hardware, firmware, and software
• Guide the creation of diagnostics, validation tools, and bring-up workflows that scale across teams and products
• Work across architecture, design, verification, drivers, performance libraries, and product teams to ensure alignment
• Provide technical mentorship and review, raising the overall effectiveness of teams working on new GPU platforms
• Capture lessons learned from new product bring-up and translate them into reusable architecture patterns, best practices, and documentation
• Leverage AI-assisted software development tools to accelerate the design, implementation, review, and documentation of complex software libraries
• Establish best practices for responsible use of AI assistance, including validation, review, and traceability of generated code and technical artifacts
• Deep experience in GPU, accelerator, or SoC architecture, including memory systems, interconnects, and scalability considerations
• History of technical leadership across distributed, cross-functional engineering teams
• Strong background in systems software, firmware, drivers, or performance software used to enable new silicon
• Proven experience in hardware/software co-design, including defining interfaces and debugging cross-layer issues
• Hands-on programming experience in C/C++ and Python
• Familiarity with low-level debugging tools and workflows
• Experience working with performance modeling, simulators, or early validation infrastructure
• Applied experience using AI-assisted coding tools in professional software engineering workflows, including code generation, refactoring, test creation, documentation, and design exploration
• Advanced degree in Computer Engineering, Electrical Engineering, Computer Science, or equivalent practical experience
• Advanced degrees, such as M.Sc., M.Eng., Ph.D. are preferred