Solutions Architect - CPU and LPU

Job Description

We're looking for a software-focused Solutions Architect to drive adoption of next-generation AI infrastructure across NVIDIA CPU platforms and LPU-based inference systems.

As a Solutions Architect, you will be the first line of technical expertise between NVIDIA and our customers for CPU- and LPU-centric AI system design. You will help customers understand how NVIDIA CPUs and LPU-based systems can improve the efficiency, latency, throughput, and total cost of their AI workloads, especially when deployed alongside NVIDIA GPUs in heterogeneous production environments.

Key Responsibilities:

• Evangelize NVIDIA CPU platforms, including Grace, Vera, and future generations, as well as LPU-based systems and LPX-class platforms, with a strong focus on AI software stacks and workload efficiency.

• Help customers design and optimize AI workloads across CPU, GPU, and LPU, improving latency, throughput, utilization, and overall cost efficiency.

• Analyze and tune LLM and generative AI pipelines across serving, runtime, memory, I/O, batching, scheduling, and orchestration layers.

• Build proof-of-concepts, reference architectures, and technical guidance in partnership with Engineering, Product, and Sales teams.

• Establish trusted technical relationships with customer architects, infrastructure teams, and senior leaders, becoming a strategic advisor for heterogeneous AI system design.

Requirements:

• MS or PhD in Computer Science, Engineering, Mathematics, Physics, or a related field, or equivalent experience, plus 5+ years in AI systems, infrastructure, performance engineering, or solution architecture.

• Strong understanding of modern CPU architecture, Linux systems, and software performance tuning, along with hands-on experience in AI inference for LLM, generative AI, or agentic AI workloads.

• Experience optimizing heterogeneous systems involving CPU and accelerators, with familiarity in frameworks such as PyTorch, Triton, TensorRT-LLM, vLLM, or ONNX Runtime.

• Strong programming, problem-solving, and communication skills, with the ability to work effectively with both technical teams and senior customer stakeholders.

Nice to Have:

• Experience with NVIDIA CPU platforms such as Grace, Grace Hopper, or Arm64 server environments, and familiarity with LPU-based systems or other low-latency inference accelerators.

• Deep expertise in LLM inference optimization, serving architecture, and workload placement across CPU, GPU, and LPU.

• Experience building customer-facing proof-of-concepts and measuring AI efficiency through latency, throughput, cost per token, power, or utilization.

• Familiarity with NVIDIA AI software and platform technologies.