Job Summary 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.

Valuable-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 (NVIDIA Nsight, Intel VTune, AMD ROCProfiler, or similar).
- Publications or patents in GPU architecture, AI systems performance, or related areas.

Job Type Experienced Hire

Shift Shift 1 (India)

Primary Location Bangalore

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.

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📌 GPU Architecture and AI Performance Simulation Engineer (Bengaluru)
🏢 Intel
📍 Bengaluru