Director of AI Workload Intelligence

About SK hynix America:

At SK hynix America, we are at the forefront of semiconductor innovation. As a global leader in HBM, DRAM, and NAND flash technologies, we develop the advanced memory solutions powering everything from advanced mobile technology to massive AI data centers. With major investments in the U.S. and a leading position in the global AI revolution, we build the critical infrastructure of the digital landscape and remain committed to sustainable operations. We invite innovative minds to be part of our journey. Here, you will be part of a collaborative team pioneering the next generation of memory technologies, expanding our market footprint, and defining the future of computing.

Work Model: Onsite

Job Title: Director of AI Workload Intelligence
Office Location: San Jose, CA
Job Type: Full-Time
Work Model: Onsite 

Job Summary:

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.

Responsibilities:

• 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.
• Identify HBF-applicable AI model families and inference data objects.
• Develop memory-use taxonomy and HBF applicability criteria.
• Provide model-driven inputs to Compiler/Runtime and AI Inference SW Stack teams.

Qualifications:

• Proven experience leading global AI system architecture initiatives and cross-functional teams.
• Hands-on experience in AI inference systems, ML systems, LLM serving, AI model analysis, AI framework/runtime analysis, or heterogeneous accelerator software.
• Strong understanding of Transformers, LLMs, MoE, recommendation models, embedding/retrieval workloads, and multimodal inference.
• Experience with PyTorch, Hugging Face, vLLM, SGLang, TensorRT-LLM, ONNX Runtime, Triton Inference Server, or equivalent AI inference stacks.