Technical Director, Large-Scale AI Model Inferencing

Samsung Semiconductor•San Jose, CA

3d•$219,000 - $351,000•Onsite

About The Position

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.

Requirements

BS in Computer/Electrical/Electronic Engineering or Computer Science, and 20 years of relevant experience MS in Computer/Electrical/Electronic Engineering or Computer Science with 18 years of relevant experience preferred.
12+ years in systems engineering, with 4+ years hands-on in large-scale LLM inference or GPU systems performance — you have personally profiled, diagnosed, and fixed memory bottlenecks in production serving, not just read about them.
First-principles understanding of transformer-class model internals: you can derive KV-cache size formulas from attention math, explain MQA/GQA/MLA tradeoffs, and reason about activation-memory peaks during prefill.
Working expertise with MoE model behavior: routing, expert parallelism, load skew, and the weight-memory economics of serving models larger than GPU capacity.
Direct experience with at least one major inference stack's memory-management internals (vLLM PagedAttention/block manager, SGLang HiCache/token pools, TensorRT-LLM KV manager, or llama.cpp compute buffers) — code-level, not configuration-level.
Strong performance-engineering skills: bandwidth-bound vs. compute-bound analysis, NUMA and PCIe topology reasoning, RDMA basics, and fluency with GPU/CPU profilers.
Track record of building systems software at the memory/storage/IO layer — caches, tiering, paging, or storage engines — with production deployments.
Ability to write models and simulators, not just measure: analytical queueing, cache-hit-rate, and bandwidth models that predict system behavior before hardware exists.
Excellent written and verbal communication, including executive-level technical narrative; comfort being the technical face of the company in front of customers.

Nice To Haves

Experience with State Space Model or hybrid SSM-attention serving (Mamba-class state management, cache swapping for recurrent models) — rare and highly valued.
Contributions to open-source inference/caching projects (vLLM, SGLang, LMCache, HiCache, Mooncake, KTransformers, llama.cpp).
Experience with CXL memory pooling, CXL-attached tiering, or near-memory processing in real deployments or serious prototypes.
Experience with SSD/NVMe as a KV or expert cache tier, including QoS engineering for inference-grade latency.
Background in memory/storage product companies bringing hardware-software co-designed solutions to market.
You’re inclusive, adapting your style to the situation and diverse global norms of our people.
An avid learner, you approach challenges with curiosity and resilience, seeking data to help build understanding.
You’re collaborative, building relationships, humbly offering support and openly welcoming approaches.
Innovative and creative, you proactively explore new ideas and adapt quickly to change.

Responsibilities

Serve as expert on how different model families consume and move memory, and translate that into memory-product requirements.
Model the memory footprint, bandwidth demand, and access patterns of frontier open-weight models (e.g., Llama/Qwen-class dense, DeepSeek/Kimi-class MoE, Jamba-class hybrids) and publish internal reference architectures for each.
Track the model landscape as a roadmap input: anticipate what coming architectures (longer contexts, agentic multi-session reuse, reasoning-loop workloads, speculative decoding drafts) will demand from memory systems 12–24 months out.
Own deep expertise in production inference stacks — SGLang (HiCache), vLLM (PagedAttention, LMCache integration), NVIDIA Dynamo, TensorRT-LLM, llama.cpp-class engines — including their memory-management internals, not just their flags.
Drive inference performance engineering: continuous batching, chunked prefill, disaggregated prefill/decode, prefix and radix caching, speculative decoding, CUDA Graphs, and their interactions with memory tiering.
Own the latency/throughput/cost envelope: TTFT and TBT/TPOT SLOs, tokens-per-second per dollar, GPU memory utilization as the binding constraint, and the tradeoff curves between cache hit rate, memory capacity, and bandwidth.
Define benchmarking and characterization methodology: realistic agentic and long-context workloads (multi-turn reuse, session persistence, RAG prefixes), KV-cache reuse-rate measurement, and bandwidth-latency profiling across the full hierarchy (Nsight, PyTorch Profiler, vendor memory tools).
Define engineering requirements, with proof, for tiered memory systems for inference at fleet scale: HBM as L1, host DRAM (pinned, NUMA-aware pools) as L2, CXL-attached memory pools as an elastic tier, and NVMe/SSD as capacity tier — with the policies (admission, eviction, prefetch, placement) that make the hierarchy behave like one memory.
Design expert-weight offloading solutions for MoE serving: host-resident expert pools, GPU-resident expert caches with bandwidth-adaptive fill/evict policies, and CPU/CXL-execution hybrid paths — informed by the routing statistics of real models.
Translate model knowledge into product: write the requirements, reference architectures, and performance models that guide memory hardware and firmware roadmaps (HBM capacity/bandwidth, CXL device behavior, SSD QoS for cache tiers), and validate with end-to-end prototypes on real inference workloads.
Develop and Deliver POCs: demos and published benchmarks showing inference TCO improvement from the memory stack — e.g., context capacity multiplied at constant GPU count, or cost-per-token reduced through cache-hit-rate gains — credible to both CTOs and PhD researchers.
Set multi-year technical strategy for AI memory solutions; own build-vs-adopt-vs-contribute decisions across the open-source inference and caching ecosystem (vLLM, SGLang, LMCache, Cognos-style KV stores) and drive upstream contributions where strategic.
Lead architecture reviews and deep-dive design sessions; write the documents that become the company's standard for how we talk about memory for AI.
Represent the company with customers and partners at the deepest technical level: serve as the expert voice in CTO-to-CTO conversations, design wins, and standards discussions.
Mentor senior engineers and grow a bench of architecture talent across the model-to-memory boundary.