Principal Software Engineer – Large-Scale LLM Memory and Storage Systems
NVIDIA· Engineer, Sys SW
Hybrid (Santa Clara, CA), Hybrid (Remote US)$272k - $431.3kfull-timePosted 7mo ago
About the role
NVIDIA Dynamo is a high-throughput, low-latency inference framework for serving generative AI and reasoning models across multi-node distributed environments. This role defines the vision and roadmap for memory management of large-scale LLM and storage systems, focusing on unified memory layers and deep integrations with leading LLM serving engines to enable efficient, resilient deployment of LLM workloads.
Responsibilities
- Design and evolve a unified memory layer that spans GPU memory, pinned host memory, RDMA-accessible memory, SSD tiers, and remote file/object/cloud storage to support large-scale LLM inference.
- Architect and implement deep integrations with leading LLM serving engines (such as vLLM, SGLang, TensorRT-LLM), with a focus on KV-cache offload, reuse, and remote sharing across heterogeneous and disaggregated clusters.
- Co-design interfaces and protocols that enable disaggregated prefill, peer-to-peer KV-cache sharing, and multi-tier KV-cache storage (GPU, CPU, local disk, and remote memory) for high-throughput, low-latency inference.
- Partner closely with GPU architecture, networking, and platform teams to exploit GPUDirect, RDMA, NVLink, and similar technologies for low-latency KV-cache access and sharing across heterogeneous accelerators and memory pools.
- Mentor senior and junior engineers, set technical direction for memory and storage subsystems, and represent the team in internal reviews and external forums (open source, conferences, and customer-facing technical deep dives).
Qualifications
- Masters or PhD or equivalent experience
- 15+ years of experience building large-scale distributed systems, high-performance storage, or ML systems infrastructure in C/C++ and Python, with a track record of delivering production services.
- Deep understanding of memory hierarchies (GPU HBM, host DRAM, SSD, and remote/object storage) and experience designing systems that span multiple tiers for performance and cost efficiency.
- Distributed caching or key-value systems, especially designs optimized for low latency and high concurrency.
- Hands-on experience with networked I/O and RDMA/NVMe-oF/NVLink-style technologies, and familiarity with concepts like disaggregated and aggregated deployments for AI clusters.
- Strong skills in profiling and optimizing systems across CPU, GPU, memory, and network, using metrics to drive architectural decisions and validate improvements in TTFT and throughput.
- Excellent communication skills and prior experience leading cross-functional efforts with research, product, and customer teams.
Skills
Required:C/C++PythonMemory hierarchy designRDMA/NVMe-oF/NVLink
Benefits
- Equity
- Benefits package (details at https://www.nvidia.com/en-us/benefits/)
Last updated Jul 3, 2026. Interested? Apply on the company site.
About NVIDIA
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NVIDIA invents the GPU and drives advances in AI, HPC, gaming, creative design, autonomous vehicles, and robotics.
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