GPU Architecture and AI Performance Simulation Engineer

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.

Good-to-Have Skills

• Experience with cycle-accurate simulators or hardware performance emulation platforms.
• Familiarity with AI framework internals (PyTorch, JAX, TensorFlow) and kernel-level optimization (CUDA, SYCL, HIP, Triton).
• Exposure to LLM inference optimization techniques (quantization, KV-cache management, speculative decoding).
• Experience with performance profiling tools (e.g., NVIDIA Nsight, Intel VTune, AMD ROCProfiler, or similar).
• Publications or patents in GPU architecture, AI systems performance, or related areas.

We're looking for talented individuals excited to solve complex challenges and shape the future of platform architecture. If you're ready to make an impact and contribute to Intel's success, we encourage you to apply today.

Job Type:

Experienced Hire

Shift:

Shift 1 (India)

Primary Location:

India, Bangalore

Additional Locations:

Posting Statement:

Position of Trust

N/A

Work Model for this Role

This role will be eligible for our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. * Job posting details (such as work model, location or time type) are subject to change.

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