3 days ago
Base Salary
$152k - $288k/yr
Responsibilities
- Design and build a multi-cloud AI data layer for dataset, checkpoint, and scratch-data placement, caching, tiering, and movement across storage backends.
- Develop high-throughput streaming loaders, distributed caches, and large-scale checkpoint save and restore paths for training and reinforcement learning workloads.
- Build Kubernetes-native APIs, operators, and storage integrations that make data available and accountable across Slurm and Kubernetes workloads.
- Own quotas, usage attribution, retention, cleanup, lifecycle management, and capacity forecasting across hundreds of research teams.
- Build I/O telemetry and access analytics to identify hot and cold data, metadata pressure, and I/O stalls.
- Participate in on-call operations, incident analysis, and reliability improvements for production data infrastructure.
Requirements
- BS or MS in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience.
- 5+ years of experience building production systems or data-infrastructure software.
- Deep experience with parallel or distributed file systems, object storage at scale, or large-scale data pipelines and data lakes.
- Strong understanding of I/O throughput, latency, metadata cost, caching, and workload access patterns.
- Strong programming ability in Go, Rust, C, or C++, with Python for tooling and analysis.
- Experience owning a production service and improving it measurably.
- Clear written and spoken communication with engineers and researchers.
- Preferred experience includes large-scale AI training data loaders, streaming datasets, checkpointing, distributed caches, AI-oriented storage, node-local NVMe tiers, quota or capacity systems, and storage for reinforcement learning or agentic workloads.
Benefits
- Eligible for equity and benefits.
- Base salary varies by location, experience, and other objective job-related factors, with stated ranges for Level 3 and Level 4.
- Applications are accepted at least until November 13, 2026.
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.