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# Senior Software Engineer, AI Data

- **Company:** NVIDIA Corporation
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $152,000 - $241,500
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon S3, Computing Platforms, C++ (Programming Language), Computer Programming, Computer Engineering, Distributed File Systems, File Systems, General Parallel File Systems, Python (Programming Language), Metadata, Distributed Caching, Weka, Ceph (Software), Graphics Processing Unit (GPU), Data Lakes, Kubernetes, Information Technology, Low Latency, Slurm, Data Pipelines, Nvme
- **Published:** November 10, 2026
- **Apply:** https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-Software-Engineer--AI-Data_JR2029702

## About the Role

* BS or MS in Computer Science, Computer Engineering, Electrical Engineering or a related field, or equivalent experience.
 * 5+ years building production systems or data-infrastructure software.
 * Deep experience with at least one of the following: parallel or distributed file systems (Lustre, WEKA, VAST, GPFS, Ceph or similar), object storage at scale (S3 or compatible), or large-scale data pipelines and data lakes.
 * A clear grasp of I/O performance: throughput, latency, metadata cost, caching and how access patterns interact with each.
 * Strong programming in Go, Rust, C or C++, plus Python for tooling and analysis.
 * A track record of owning a service in production and making it measurably better.
 * Clear written and spoken communication with engineers and researchers.

Ways to stand out from the crowd

 * Experience feeding large-scale AI training: data loaders, streaming datasets, and storage behavior at thousands of GPUs.
 * Experience with checkpointing at scale: asynchronous or distributed checkpoints, tiered checkpoint storage, fast restart after failure.
 * Built or ran a distributed cache or AI-oriented storage system, or used node-local NVMe as a managed tier.
 * Built a quota, accounting or capacity system that many teams depended on.
 * Knowledge of RL and agentic workloads and what they need from storage: many small reads, environment snapshots, sandbox images and fast fan-out.

## Description

* Make data a first-class part of the compute platform. Build Kubernetes-native APIs, operators and storage integrations so a job declares the data it needs, and the platform mounts, caches and accounts for it, on Slurm and on Kubernetes.
 * Own quotas, capacity and data lifecycle across hundreds of research teams: usage attribution, quota enforcement, retention, clean-up of stale data, and forecasting capacity before it runs out.
 * Measure how workloads use data. Build I/O telemetry and access analytics that show hot and cold data, metadata pressure and I/O stalls, and use them to drive placement and fixes.
 * Own reliability for what you build: on-call, incident analysis, and changes that stop whole classes of data incidents, such as full file systems, runaway metadata load and lost checkpoints.