AI System Performance Engineer

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Intel Recruitment 2026: Intel Recruitment 2026 is hiring AI System Performance Engineers in Bangalore. Check eligibility, job role, skills, salary, selection process, and apply online for the latest Intel careers.Recruitment services

About Company: At Intel, building a better world is our business. Our mission is to utilize the power of Moore’s Law to bring smart, connected devices to every person on earth while serving as a role model for how companies should operate. We power amazing experiences through a diverse product line and exciting partnerships. Our strategy is a virtuous cycle of innovation—the cloud and the data centre, the Internet of Things, memory and FPGAs all bound together by the goal of greater connectivity and enhanced performance.Job search platform

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Job Title: AI System Performance Engineer

Location: Bangalore

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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.

Requirements:

•  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.

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.

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Work Experience: 1+ Years

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How to Apply for Intel Recruitment 2026?

Interested and Eligible candidates can apply for this drive online by using the link below.Recruitment services

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