AI Data Engineer

NVIDIA · Santa Clara, CA · 152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4 NVIDIA's Managed AI Research Superclusters (MARS) team is looking for an AI Data Engineer to help build and scale the infrastructure, platforms, and tools that enable researchers and engineers to develop the next generation of AI/ML systems. The MARS AI Data team owns how NVIDIA's AI research uses data: many petabytes of training data, checkpoints and datasets across multiple clouds and partners. The successful candidate will help design MARS' data platform on Kubernetes from the ground up, working directly with the researchers training NVIDIA's own models. Responsibilities: Design and build the AI data layer for a multi-cloud GPU fleet: how datasets, checkpoints and scratch data are placed, cached, tiered and moved between different storage backends. Build high-throughput data paths for training and RL, including streaming loaders from object storage, distributed caches, and checkpoint save and restore at thousands of GPUs. Make data a first-class part of the compute platform, building Kubernetes-native APIs, operators and storage integrations. Own quotas, capacity and data lifecycle across hundreds of research teams: usage attribution, quota enforcement, retention, clean-up of stale data, and forecasting capacity. Measure how workloads use data, building I/O telemetry and access analytics. Own reliability for what you build: on-call, incident analysis, and changes that stop whole classes of data incidents. Requirements: 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: parallel or distributed file systems, object storage at scale, or large-scale data pipelines and data lakes. 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. Preferred Qualifications: 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. The base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4.