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Job Summary:
AMD is a leading company focused on building innovative products that enhance computing experiences across various domains. The Principal GPU/NPU AI System Architect will define and drive AI system architecture for embedded and edge platforms, with a focus on GPU/NPU micro-architecture and AI software stacks, ensuring efficient AI deployments in robotics, automotive, and industrial markets.
Responsibilities:
• Develop deep architectural understanding of GPU, NPU, and heterogeneous SoC designs, including memory hierarchies, interconnects, scheduling, and power/performance trade‑offs.
• Guide HW–SW co‑optimization strategies for AI workloads across vision, perception, planning, and control.
• Influence silicon and platform roadmaps using model‑driven architectural insights from robotics, automotive, and industrial workloads.
• Collaborate across silicon, system engineering, software, thermal/mechanical, security, and product teams.
• Technically lead internal AI engineers and work closely with partners, ISVs, and customers.
• Act as a technical authority and mentor, influencing architecture decisions without direct reporting authority.
• Architect AI solutions with strong understanding of model internals (CNNs, Transformers, multi‑modal models, sensor fusion, perception stacks).
• Evaluate and map model characteristics (latency, memory bandwidth, precision, sparsity) onto GPU/NPU execution.
• Drive model optimization strategies (quantization, pruning, distillation, compilation flows) aligned with embedded constraints.
• Define and optimize AI software stacks spanning: Frameworks (PyTorch, ONNX, TensorRT‑like runtimes), Compilers, graph optimizers, and runtime schedulers, Drivers, firmware, and OS integration.
• Lead solutioning for edge and embedded deployment, including OTA updates, lifecycle management, and production‑grade robustness.
• Ensure scalability from prototype → production → long‑term maintenance.
• Translate domain use‑cases into architectural requirements and reusable platform capabilities.
Qualifications:
Required:
• Deep expertise across GPU/NPU architecture, AI software stacks, and model behavior.
• Experience in translating real-world robotics, automotive, and industrial workloads into scalable, production-ready AI platform architectures.
• Hardware-aware AI model understanding with embedded deployment experience.
• Ability to drive full-stack architectural trade-offs across performance, power, memory, safety, and lifecycle constraints.
• Strong architectural judgment and cross-functional influence without direct authority.
• Ability to bridge research, productization, and long-term platform evolution.
• Develop deep architectural understanding of GPU, NPU, and heterogeneous SoC designs, including memory hierarchies, interconnects, scheduling, and power/performance trade-offs.
• Guide HW–SW co‑optimization strategies for AI workloads across vision, perception, planning, and control.
• Influence silicon and platform roadmaps using model‑driven architectural insights from robotics, automotive, and industrial workloads.
• Collaborate across silicon, system engineering, software, thermal/mechanical, security, and product teams.
• Technically lead internal AI engineers and work closely with partners, ISVs, and customers.
• Act as a technical authority and mentor, influencing architecture decisions without direct reporting authority.
• Architect AI solutions with strong understanding of model internals (CNNs, Transformers, multi‑modal models, sensor fusion, perception stacks).
• Evaluate and map model characteristics (latency, memory bandwidth, precision, sparsity) onto GPU/NPU execution.
• Drive model optimization strategies (quantization, pruning, distillation, compilation flows) aligned with embedded constraints.
• Define and optimize AI software stacks spanning frameworks (PyTorch, ONNX, TensorRT‑like runtimes), compilers, graph optimizers, and runtime schedulers, drivers, firmware, and OS integration.
• Lead solutioning for edge and embedded deployment, including OTA updates, lifecycle management, and production‑grade robustness.
• Ensure scalability from prototype → production → long‑term maintenance.
• Translate domain use‑cases into architectural requirements and reusable platform capabilities.
• Bachelor’s or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent.
Preferred:
• Deep expertise in GPU and/or NPU architecture and execution models.
• Strong hands‑on experience with AI models and inference pipelines, not just framework usage.
• Proven background in embedded / edge AI systems.
• Strong understanding of hardware‑aware model optimization techniques.
• Experience in robotics, automotive, or industrial AI domains.
• Ability to translate customer problems into scalable architectural solutions.
• Motivating leader with good interpersonal skills; cross‑functional & external leadership.
Company:
Advanced Micro Devices is a semiconductor company that designs and develops graphics units, processors, and media solutions. Founded in 1969, the company is headquartered in Santa Clara, USA, with a team of 10001+ employees. The company is currently Late Stage.