NVIDIA

NVIDIA

Posted via Workday

Principal Software Engineer – Large-Scale LLM Memory and Storage Systems

Posted Aug 5, 2026

Role at a glance

Salary
$272K – $431.3K/yr
Location
3 Locations, California, United States
Work arrangement
On-site
Employment
Full-time
Experience
15+ years of experience building large-scale distributed systems, high-performance storage, or ML systems infrastructure
Education
Masters or PhD or equivalent experience

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Role Summary

AI-generated

The Principal Systems Engineer will define the vision and roadmap for memory management within NVIDIA Dynamo, a high-throughput, low-latency inference framework for generative AI and reasoning models across multi-node distributed environments. The role focuses on unified memory and storage systems that support efficient KV-cache management and large-scale LLM inference across heterogeneous clusters.

What You'll Do

  • Design and evolve a unified memory layer spanning GPU memory, pinned host memory, RDMA-accessible memory, SSD tiers, and remote file,...
  • Architect and implement integrations with LLM serving engines including vLLM, SGLang, and TensorRT-LLM for KV-cache offload, reuse, and...
  • Co-design interfaces and protocols for disaggregated prefill, peer-to-peer KV-cache sharing, and multi-tier KV-cache storage.
  • Partner with GPU architecture, networking, and platform teams to use GPUDirect, RDMA, NVLink, and similar technologies for low-latency...
  • Mentor engineers, set technical direction for memory and storage subsystems, and represent the team in internal reviews and external forums.

Generated from the employer's posting. Verify important details before applying.

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Qualifications

C/C++ and Python; large-scale distributed systems, high-performance storage, or ML systems infrastructure; memory hierarchies spanning GPU HBM, host DRAM, SSD, and remote/object storage; distributed caching or key-value systems; networked I/O and RDMA/NVMe-oF/NVLink-style technologies; profiling and optimizing CPU, GPU, memory, and network systems; excellent communication skills; leading cross-functional efforts.

Required

  • C/C++ and Python
  • Large-scale distributed systems, high-performance storage, or ML systems infrastructure
  • Memory hierarchies spanning GPU HBM, host DRAM, SSD, and remote/object storage
  • Distributed caching or key-value systems
  • Networked I/O and RDMA/NVMe-oF/NVLink-style technologies
  • Profiling and optimizing systems across CPU, GPU, memory, and network
  • Excellent communication skills
  • Leading cross-functional efforts with research, product, and customer teams

Preferred

  • Contributions to open-source LLM serving or systems projects focused on KV-cache optimization, compression, streaming, or reuse
  • Designing unified memory or storage layers across GPU, host, SSD, and cloud tiers
  • Publications or patents in LLM systems, memory-disaggregated architectures, RDMA/NVLink-based data planes, or KV-cache/CDN-like systems...

Original job description

Content provided by the employer

NVIDIA Dynamo is a high-throughput, low-latency inference framework for serving generative AI and reasoning models across multi-node distributed environments. Built in Rust for performance and Python for extensibility, Dynamo orchestrates GPU shards, routes requests, and manages shared KV cache across heterogeneous clusters so that many accelerators feel like a single system at datacenter scale. As large language models rapidly outgrow the memory and compute budget of any single GPU, this platform enables efficient, resilient deployment of cutting-edge LLM workloads.


We are seeking a Principal Systems Engineer to define the vision and roadmap for memory management of large-scale LLM and storage systems.


What you'll be doing:

  • 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).

What we need to see:

  • 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.

Ways to stand out from the crowd:

  • Prior contributions to open-source LLM serving or systems projects focused on KV-cache optimization, compression, streaming, or reuse.

  • Experience designing unified memory or storage layers that expose a single logical KV or object model across GPU, host, SSD, and cloud tiers, especially in enterprise or hyperscale environments.

  • Publications or patents in areas such as LLM systems, memory-disaggregated architectures, RDMA/NVLink-based data planes, or KV-cache/CDN-like systems for ML.

With highly competitive salaries and a comprehensive benefits package, NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to outstanding growth, our special engineering teams are growing fast. If you're a creative and autonomous engineer with a genuine passion for technology, we want to hear from you!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until January 13, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

NVIDIA

About the company

NVIDIA

Large Enterprise

NVIDIA is a leading technology company renowned for its graphics processing units (GPUs) and innovative computing solutions that enhance visual experiences across multiple platforms, including gaming, scientific research, and artificial intelligence. Founded in 1993, the company has expanded its offerings to include powerful AI frameworks and deep learning platforms, making significant contributions to industries such as gaming, data centers, automotive, and healthcare. NVIDIA's commitment to pushing the boundaries of visual computing continues to drive advancements in both hardware and software, positioning the company at the forefront of emerging technologies and digital transformation.