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# Staff Engineer - Memory/Network Systems Software

- **Company:** Nthe Varsity Tutors
- **Location:** San Jose, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Systems Engineering, C++ (Programming Language), Software Debugging, Linux, Distributed Systems, Dynamic Random-Access Memory, Github, Python (Programming Language), Linux Kernel, Open Source Technology, Performance Tuning, Software Engineering, Pytorch, Large Language Models, HNSW (Hierarchical Navigable Small World), Non-Uniform Memory Access, Gitlab, Perf (Linux), Kubernetes, Free and Open-Source Software, Milvus, Machine Learning Operations, FAISS, TensorRT, VLLM, Nvme, Golang
- **Published:** October 3, 2026
- **Apply:** https://www.thejobnetwork.com/job/e8e77c43-ef05-483f-a362-1f864c400dbc/senior-staff-engineer-staff-engineer-linux-kubernetes-ai-systems

## About the Role

* Strong systems performance engineering background.
 * Deep experience with NVMe, block layer, page cache, async I/O.
 * Experience with embeddings/vector DBs (FAISS, Milvus, DiskANN, HNSW).
 * Linux tracing tools and GPU/accelerator I/O knowledge.
 * Open-source contributions strongly preferred, * Deep Linux kernel + host OS experience.
 * Strong Kubernetes internals (operators, controllers, CRDs, kubelet).
 * Go programming experience.
 * Distributed systems experience.
 * NUMA, cgroups, device/resource management.
 * Open-source contributions strongly preferred.

Preferred Candidate Profile

 * Active in open-source communities (Linux, Kubernetes, Go, benchmarking tools).
 * GitHub/GitLab contributions, PRs, issues, or maintainership.
 * Strong systems engineering background.
 * Comfortable working onsite in San Jose.

## Description

* Benchmark AI/ML workloads across memory (HBM, DRAM, CXL) and storage (SSD/NAND).
 * Analyze NVMe SSD and storage-stack performance.
 * Work with LLM training/inference tools (PyTorch, vLLM, TensorRT-LLM).
 * Use Linux performance tools (perf, eBPF, blktrace, fio, etc.).
 * Build reproducible benchmark automation in Python; systems work in C/C++.
 * Provide performance insights to leadership for product roadmap.
 * Run industry benchmarks (MLPerf Storage or similar)., * Build end-to-end systems software across Linux + Kubernetes.
 * Work across kernel, user-space services, node agents, kubelet, CRDs, controllers.
 * Build Kubernetes operators/controllers using Go, client-go, controller-runtime.
 * Develop resource lifecycle workflows (discover, reserve, activate, resize, rollback, reconcile).
 * Integrate memory lifecycle, hotplug, NUMA, cgroups, device management.
 * Define interfaces between host software and Kubernetes control-plane.
 * Build distributed systems for reconciliation, idempotency, fault recovery.
 * Support debugging, bring-up, validation, and performance tuning.