Technical Director, Large-Scale AI Model Inferencing

The posting, as Samsung Semiconductor wrote it

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What You’ll Do

Inference is becoming a memory-bandwidth business. As models scale past what any single GPU can hold — KV caches grow with context, MoE expert weights spill beyond HBM, and new architectures change the rules of what "model state" even means — the winners will be the companies that treat memory as the core product of AI inference , not an afterthought.

We are looking for a Hands-on Principal Engineer who combines deep, first-principles knowledge of AI model architectures (dense Transformers, Mixture-of-Experts, State Space Models, and hybrids) with production-scale inference expertise , to own the requirement for full-stack AI memory solutions at scale — spanning GPU HBM, host DRAM, CXL-attached memory pools, and NVMe/SSD tiers and Samsung Cognos, AI memory software that moves model state intelligently across them.

This person will be the technical authority who connects model behavior to memory-system design: someone who can explain why an MoE router's activation pattern dictates an LRU expert cache policy, why a Mamba state cache breaks the assumptions of PagedAttention, and why disaggregated prefill/decode changes the required memory bandwidth per token by an order of magnitude — and then build the products that exploit those facts.

Level: Principal Engineer
Team: Memory Solutions Lab / Data Fabric Solutions
Reports to: Chief Technologist, Memory Solutions Lab

Location: Daily onsite presence preferred at our San Jose office/headquarters in alignment with our Flexible Work policy; remote/hybrid option available.

Job ID : 43027

Model Architecture Expertise — The Foundation

• Serve as expert on how different model families consume and move memory, and translate that into memory-product requirements:
• Dense Transformers : MHA/MQA/GQA/MLA attention, KV-cache growth characteristics, long-context behaviors, attention sinks and prefix locality.
• Mixture-of-Experts : routed vs. shared experts, expert-parallel execution, routing skew and hot-expert locality, expert-weight offloading and cache-admission policies, per-token weight-read economics.
• State Space Models (Mamba/Mamba-2) and hybrid SSM-attention architectures : recurrent state

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