GPU Architecture and AI Performance Simulation Engineer

About the role

Job Details:

Job Description:

About the Role

We are looking for a talented and motivated GPU Architecture and AI Performance Simulation Engineer. The role sits at the intersection of AI workloads and next-generation GPU hardware - analyzing, modeling, and optimizing AI workload performance on GPU and SoC architectures to shape architectural decisions across current and future silicon. The candidate will guide the team in fine-tuning workload, SoC, and system models, propose data-backed architectural enhancements, and track the AI GPU market to inform Intel's future accelerator roadmap. Strong analytical skills, the ability to present performance findings and trade-offs to engineering and leadership teams, and a willingness to mentor peers are essential.

Key Responsibilities

• Analyze AI workload behavior on GPU architectures - distinguishing compute-bound, memory-bound, and cache-bound behavior to identify performance bottlenecks and optimization opportunities.

• Build and maintain roofline analytical models and cycle-accurate performance models to support hardware what-if studies and guide micro-architectural decisions.

• Analyze AI workloads built on Transformer and Mixture-of-Experts (MoE) architectures, inference and training, and assess the hardware-utilization impact of kernel-level optimizations such as Flash Attention, Flash Decode, Sparse Attention, and Causal Masking.

• Model and analyze multi-GPU and at-scale AI systems - workload distribution, synchronization overhead, and communication-compute overlap.

• Drive workload and architectural bottleneck analysis for the early architecture definition phases of Intel's future AI GPU accelerator roadmap products.

• Guide and mentor the team in delivering analytical models and end-to-end system-level performance projections; review modeling methodology and drive consistency across studies.

• Propose new architectural enhancement ideas, backed by quantitative modeling data and workload evidence.

• Maintain a forward-looking view of the AI GPU and accelerator market - tracking competitive roadmaps, whitepapers, and conferences - and translate those trends into architectural requirements while keeping the team updated on applicable AI innovations.

Qualifications:

Required Qualifications

• Bachelor's with 10+ yrs, master’s with 8+ years and Ph.D with 5+ years of experience in System/GPU/SoC performance engineering, AI workload analysis, or hardware architecture.

• Deep knowledge of GPU microarchitecture (NVIDIA SM/CUDA cores, Intel Xe cores, or equivalent), GPU execution pipelines, and cache hierarchy design (L1/L2/L3).

• Familiarity with SoC design principles, on-chip fabric, memory subsystems, and interconnects.

• Experience with multi-GPU system architectures (NVLink, PCIe, CXL, or equivalent) and scaling AI workloads across multiple devices.

• Good understanding of distributed training/inference techniques: Tensor Parallel, Data Parallel, Pipeline Parallel, Expert Parallel, and Model Split methodologies.