AI System Performance Architect
Intel Corporation (company site, opens in a new tab)8+ yrsBengaluruOn-siteDirect career pageEst. 40–70 LPA
Posted 13h ago
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Job description
Structured overview of role & requirements
About This Role
- Own the end-to-end performance modeling and multi-GPU simulation of AI training and inference systems to identify bottlenecks and recommend optimal Intel system configurations.
- Model inferencing strategies and parallelism communication patterns to drive studies aligned with SLA targets such as batch size, TTFT, TPOT, ISL/OSL, and TPS.
- Perform what-if analyses on core count, memory/cache bandwidth, fabric bandwidth, GPU interconnect, and deployment options to deliver architectural guidance for future AI platforms at Intel.
Minimum Requirements
- Bachelor's, Master's or PhD in Electrical Engineering, Computer Engineering, Computer Science, or related field.
- Work Experience Required: Bachelors with 8+ years, Master’s with 6+ years or PhD with 3+ years in system performance modelling, AI workload analysis, or hardware architecture.
- Proficiency in Python for building analytical models and simulation frameworks.
- Strong understanding of LLM inference/training mechanics, distributed parallelism communication patterns, and familiarity with multi-GPU interconnect architectures (e.g., NVLink, PCIe, CXL).
Ideal Candidate Profile
- Experienced in translating AI system SLA and deployment targets into concrete architectural recommendations for multi-GPU/accelerator environments.
- Comfortable with analytical modeling, simulation, and performance analysis in AI hardware/platform contexts with a focus on inference and training workflows.
- Familiar with SoC and platform fundamentals including core scaling, cache/memory bandwidth, and fabric behaviour with hands-on exposure to AI accelerators or semiconductor hardware products.