Technical Director, Large-Scale AI Model Inferencing

Overview

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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 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.
• Emerging architectures: linear attention, sliding-window/hybrid layers, diffusion and multimodal transformers — and how each changes the memory hierarchy math.
• 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.
• Large-Scale Inference Expertise 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).
• Full-Stack AI Memory Solutions — The Core Mandate 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.
• Technical Leadership 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.
•   What You Bring BS in Computer/Electrical/Electronic Engineering or Computer Science, and 10+ years of relevant experience MS in Computer/Electrical/Electronic Engineering or Computer Science with 8 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-leve.