Develop and extend C++ and Python simulators that model system-level network and compute traffic for agentic LLM workloads in datacenter environments
Characterize real-world LLM serving workloads and distill them into representative simulator inputs
Run simulations at scale and apply statistical techniques to post-process and interpret results
Identify performance bottlenecks and translate findings into concrete architectural recommendations
Collaborate with hardware, software, and research teams to influence the design of future AI systems

Pursuing or recently completed a MS, or PhD in CS, EE, Mathematics, or a related field (or equivalent experience)
Strong programming skills in C++ and Python
Solid foundations in queueing theory and traffic modeling (e.g., Erlang models, Little's Law)
Strong statistics background: characterize huge datasets with percentiles, distributions, and clustering techniques such as K-means
Understanding of deep learning fundamentals, LLMs, and modern inference serving frameworks

Systems Performance Engineer – Agentic AI Workloads

Key skills