Develop and enhance analytical performance models and simulation frameworks for AI accelerators, GPU architectures, and large-scale AI systems. Model end-to-end AI training and inference workloads, including compute kernels, attention, MoE, memory access patterns, and communication operations. Evaluate new architectural features, compute and memory hierarchies, interconnects, and collective communication mechanisms to identify performance and efficiency improvements. Perform workload mapping and parallelization analysis across tensor, pipeline, expert, data, and context parallelism strategies to optimize accelerator utilization and scalability. Develop system-level performance models to evaluate throughput, latency, perf/W, and TCO across accelerator, rack, and cluster configurations. Correlate analytical model predictions with silicon measurements, kernel benchmarks, and software traces to validate assumptions, improve model fidelity, and identify performance bottlenecks. Build and automate performance modeling, design-space exploration, sensitivity analysis, and visualization tools to accelerate architectural evaluation. Collaborate with hardware and software teams to translate modeling insights into actionable architecture recommendations and HW-SW co-design opportunities. Master's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 3+ years technical engineering experience OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 5+ years technical engineering experience OR equivalent experience Doctorate in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 3+ years technical engineering experience OR Master's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 6+ years technical engineering experience OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 8+ years technical engineering experience OR equivalent experience Familiarity with AI agentic systems & flow for performance modeling & anlaysis 2+ years of experience in Computer Architecture, AI Systems, or closely related technical domains. Strong understanding of GPU and AI accelerator architectures, including compute pipelines, memory hierarchies, interconnects, and parallel execution models. Experience developing analytical performance models, roofline models, architectural simulators, or system-level performance estimation frameworks. Knowledge of AI training and inference workloads, including transformers, attention mechanisms, MoE architectures, and large-scale distributed execution. Experience modeling compute, memory bandwidth, communication collectives, and computation-communication overlap in accelerator-based systems. Familiarity with parallelization strategies, including tensor, expert, pipeline, data, and context parallelism, and their performance implications. Strong programming skills in Python and C/C++, with experience developing modeling frameworks, simulators, and performance analysis tools. Experience with performance profiling, benchmarking, and correlation of analytical models with hardware measurements and software traces. Familiarity with PyTorch, GPU programming, and modern AI inference or training frameworks such as vLLM, SGLang, and distributed runtime systems. Experience evaluating architectural design trade-offs using performance, power efficiency, scalability, and TCO metrics. Strong analytical, problem-solving, and communication skills, with the ability to translate performance insights into architectural recommendations.