GPU Architecture and AI Performance Simulation Engineer

Intel Technology India Pvt Ltd; Bengaluru; Hybrid; INR 3,500,000 - 5,200,000; Full time

Job summary

Intel Technology India Pvt Ltd in Bengaluru is seeking a GPU Architecture and AI Performance Simulation Engineer. The role focuses on analyzing AI workloads on GPU/SoC architectures, building models, and guiding architectural decisions for future silicon generations.

The candidate will mentor peers, track AI GPU market trends, and translate data into architectural requirements while collaborating with leadership teams.

Qualifications

• 10+ years in System/GPU/SoC performance engineering, AI workload analysis, or hardware architecture.
• Deep knowledge of GPU microarchitecture and cache hierarchy (L1/L2/L3).
• Familiarity with SoC design principles, on-chip fabric, and memory subsystems.
• Experience with multi-GPU system architectures and scaling AI workloads.
• Understanding of distributed training/inference techniques.

Responsibilities

• Analyze AI workload behavior on GPU architectures to identify bottlenecks and optimization opportunities.
• Build and maintain roofline and cycle-accurate models to support hardware what-if studies.
• Analyze Transformer and MoE workloads, including kernel-level optimizations like Flash Attention.
• Model multi-GPU at-scale systems including workload distribution and synchronization.
• Drive workload and bottleneck analysis in early architecture definition phases for future Intel AI GPUs.
• Mentor the team and ensure consistency across analytical studies.

Skills

GPU architecture

AI performance analysis

System/GPU/SoC performance engineering

Multi-GPU systems

Distributed training/inference

Education

Bachelor's degree with 10+ years

Master’s with 8+ years

Ph.D with 5+ years

Tools

CUDA

SYCL

HIP

Triton

NVIDIA Nsight

Intel VTune

AMD ROCProfiler

Job description

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.

Job Type: Experienced Hire Shift: Shift 1 (India) Primary Location: India, Bangalore Additional Locations:

Posting Statement: All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.

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.

* ADDITIONAL INFORMATION: Intel is committed to Responsible Business Alliance (RBA) compliance and ethical hiring practices. We do not charge any fees during our hiring process. Candidates should never be required to pay recruitment fees, medical examination fees, or any other charges as a condition of employment. If you are asked to pay any fees during our hiring process, please report this immediately to your recruiter.