Evaluate data placement, migration, prefetching, compression, and tiering approaches for KV cache, model state, and other AI data.
Build and validate performance models, simulations, traces, and reusable benchmarking tools to forecast future system requirements.
Lead cross-functional projects and transform outcomes into product requirements, develop recommendations, publications, demonstrations, and customer mentorship
Minimum Qualifications
Bachelor’s degree with at least 10 years of experience; master’s degree with 5 or more years; or Ph.D. with demonstrated experience in a related field.
Hands-on experience profiling and benchmarking multi-GPU AI systems across single-node and multi-node environments.
Understanding of transformer workloads, memory behavior, distributed systems, and agentic AI infrastructure.
Experience with PyTorch, an AI serving platform, Python, and at least one of C, C++, CUDA, or Triton.
Ability to lead controlled experiments and communicate results to technical and business audiences.
Preferred Qualifications
Experience with memory tiering, KV-cache management, or emerging AI workloads.
Experience with disaggregated infrastructure or performance modeling and simulation.
Relevant research, patents, open-source contributions, or other substantial technical work.
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