Director of AI Workload Intelligence
SK hynix America, San Jose, CA
The role
Focused on bridging AI intelligence with memory strategy, this Director-level position leads global initiatives to analyze LLM and multimodal workloads for HBF applicability. The leader will define data placement criteria, develop HBF-aware benchmark suites, and guide product strategy by translating model behaviors into concrete memory system values.
What you will do
- Lead global collaboration and drive AI workload-based HBF technology development from research to PoC and productization.
- Analyze AI models, algorithms, and workload trends for HBF applicability.
- Classify HBF-relevant memory objects such as weights, KV cache, activations, embeddings, adapters, and MoE experts.
- Define model-level criteria for data placement, caching, prefetching, and offload decisions.
- Analyze HBF integration points in vLLM, SGLang, TensorRT-LLM, PyTorch, and ONNX Runtime.