AI System Performance Engineer

We are looking for a talented and motivated AI System Performance Engineer. The role builds a strong understanding of AI training and inference systems and helps the team develop analytical models that project end-to-end system performance. Working across serving strategies, parallelism, collective communication, and interconnect behavior, the candidate uses these models to locate the architectural bottlenecks that limit performance and to recommend optimal system configurations for Intel architecture. A self-driven, hands-on learning by doing mindset and the ability to pick up unfamiliar workloads, tools, and architectures quickly 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, and 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.

Required 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.
• 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.

Good-to-Have Skills

• 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.
• Familiarity with Git/GitHub, CI/CD, and automated workflows.
• Understanding of power fundamentals and power/performance co-optimization.