Responsibilities

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.
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 vs. KV cache semantics, state size per sequence and per layer, cache-swapping behavior for context switching and batching, and what "cache-aware scheduling" means when the state is a fixed-size tensor instead of a token-indexed table.

Technical Director, Large-Scale AI Model Inferencing at Samsung Semiconductor · LumiJobs

Technical Director, Large-Scale AI Model Inferencing

Responsibilities

Qualification

Preferred

Benefits

Company

Samsung Semiconductor

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