Jobs in the United States

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Senior Software Engineer, AI Data

CA, United States
MA, United States
TX, United States
NC, United States

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.

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Job details

Pay: 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.
Posting language: English
Posted: Nov 9, 2026

Original job description

NVIDIA is a pioneer in accelerated computing, known for inventing the GPU and driving breakthroughs in gaming, computer graphics, high-performance computing, and artificial intelligence. Our technology powers everything from generative AI to autonomous systems, and we continue to shape the future of computing through innovation and collaboration. Within this mission, our team, Managed AI Research Superclusters (MARS), builds and scales the infrastructure, platforms, and tools that enable researchers and engineers to develop the next generation of AI/ML systems. By joining us, you'll help build solutions that power some of the planet's most advanced computing workloads.

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. We build the layer between storage and the workload. That covers data placement across file systems, object storage, and local NVMe. It also includes streaming loaders and caches that supply tens of thousands of GPU cores. Efficient checkpoint management. Quotas remain fair throughout numerous research groups. This is a builder's role on a team that is redefining what it does. You will help design MARS' data platform on Kubernetes from the ground up, working directly with the researchers training NVIDIA's own models. The work shows up as faster training steps, shorter restarts and fewer runs lost to complete storage volumes.

What you'll be doing

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. That means streaming loaders from object storage, distributed caches, and checkpoint save and restore at thousands of GPUs, including peer-to-peer and local-first restore.
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.

What we need to see

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.

Your 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. You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until November 13, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive 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.

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More about this job

Skills

MARS
Kubernetes
Research
Forecasting
Computer science
Computer engineering
Electrical engineering
Go
Rust
C
C++
Python
Communication skills

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