AI System Performance Engineer

About the Role

We are looking for a talented and motivated AI System Performance Engineer. This role involves building a strong understanding of AI training and inference systems and developing analytical models to project end-to-end system performance. The candidate will work across serving strategies, parallelism, collective communication, and interconnect behavior to identify architectural bottlenecks and recommend optimal system configurations for Intel architecture. A self-driven, hands-on learning mindset and the ability to quickly grasp unfamiliar workloads, tools, and architectures are essential.

Key Responsibilities

• Enable new AI workloads in analytical simulators and model emerging multi-GPU deployment, parallelization, and communication strategies as simulator capabilities.
• Build analytical models of newly proposed GPU architectures (GPU core, memory hierarchy, training/inference deployment scenarios) and produce performance estimates that expose architectural bottlenecks and drive improvement recommendations.
• Integrate and modify open-source simulators or analytical models for Intel-specific architecture.
• Perform what-if analysis by sweeping core count, memory/cache capacity and bandwidth, fabric bandwidth, GPU interconnect, and deployment options; deliver optimization and optimal architecture recommendations.

Requirements & Tech Stack

• Strong proficiency in Python, with the ability to build modular, maintainable analytical models and simulation frameworks.
• In-depth understanding of computer architecture: GPU/CPU, ALU/SM core, cache, and different memory types and hierarchy.
• In-depth understanding of LLM AI workloads (Transformer, MoE, Attention, MLA - LLaMA, DeepSeek, Qwen, and robotic workloads) and kernel internals.
• Familiarity with Git/GitHub, CI/CD, and automated workflows.
• Understanding of power fundamentals and power/performance co-optimization.

Qualifications

• BTech or Master's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field.
• College graduate or 1+ years of experience in AI workload and GPU architecture modelling/simulation or a similar field.
• Understanding of distributed training/inference and parallelism strategies (data, pipeline, tensor, and expert) and their communication patterns when scaling across multi-GPU hardware.
• Understanding of LLM inference and training mechanics: prefill vs. decode, KV cache, batching policies, and quantization.
• Exposure to TRT-LLM, vLLM, or SGLang.