Senior System Architect – Gpu

Senior System Architect – Gpu

Requirements

• Master’s/PhD in Computer Engineering, Computer Science or related fields (or equivalent experience)
• A minimum of 8 years of relevant work experience in GPU or CPU System Architecture development
• Proficiency in data analysis (Python, Excel) to correlate configuration changes with performance metrics
• Deep understanding of accelerated computing and AI data center requirements and tradeoffs, including performance bottlenecks, TCO, Power Delivery Network (PDN), DC Networking, etc
• Strong communication and interpersonal skills, as well as the ability to thrive in a dynamic, collaborative, distributed team.

Responsibilities

• Develop GPU architecture innovations and improvements, optimizing along the axes of performance, power efficiency, complexity, area, yield, effort, and schedule.
• Evaluate and benchmark GPU configurations (core counts, memory bandwidth, interconnect topologies) from employing different advanced packaging technologies, and identifying optimal designs for different future data center workloads.
• Develop and enhance performance analysis infrastructure, including performance simulators, testbench components and analysis tools, to evaluate configurations under different constraints.
• Implement and maintain high-level functional and performance models. Analyze application workloads and performance simulation results to identify areas of architecture improvements.
• Document decisions in system architecture specifications, working with multi-functional teams in the organization including ASIC design, software, and VLSI to review, and explore architecture trade-offs, define overall solutions and track development progress.
• Collaborate with other functional teams (ASIC, Floorplan Designs, Package Designs and Systems Engineering, etc) to validate packaging choices against performance, cost, and scalability targets.

Preferred Qualifications

• Experience with GPU architecture, especially in off-chip IO, memory subsystem, and/or Network-on-Chip (NoC)/Interconnect. Knowledgeable in system level functions such as reset and boot, DFT, and/or power management
• Expertise in analyzing performance scaling and bottlenecks at device and system levels for AI/accelerated computing workloads
• Knowledgeable in advanced packaging technologies, and their costs and benefits
• Consistent track record of efficiently implementing complex architectural features
• Exceptional problem-solving skills with a focus on optimizing performance, area, complexity, and power.