Jobs at NVIDIA

Lead invention of AI-driven ASIC development methodology, identify cross-stage bottlenecks, build scalable data pipelines, define metrics, mentor engineers, and evaluate AI/EDA research to improve predictability and cycle time.

Lead planning and execution for GPU-accelerated data science and processing libraries. Drive cross-functional release and compliance programs, manage dependencies and risks, improve dev processes and tooling, build program metrics, and ensure high-quality, predictable open-source releases.

Lead technical partnership with Alphabet teams to drive adoption and integration of NVIDIA libraries, models, and frameworks. Coordinate cross-functional engineering and GTM teams, resolve integration challenges, influence product roadmaps, and deliver joint solutions that accelerate partner workloads and developer adoption.

Lead performance practices for large-scale AI systems, enhance datacenter products, and resolve complex performance issues through collaboration and technical expertise.

The role involves performing data analysis for datacenter applications, using machine learning for GPU systems, and providing insights on trends to enhance workflows.

The Senior Production Engineer will scale AI infrastructure, manage production systems, implement monitoring capabilities, and ensure reliability and performance across NVIDIA's AI clusters.

The Senior HPC Architect role involves deploying large-scale GPU compute clusters, optimizing system administration, and collaborating with researchers on HPC and AI solutions.

Design and implement high-performance, scalable storage systems and client libraries for AI workloads. Build data loading, checkpointing, caching, POSIX-style and object-store integrations, observability and telemetry, and validate performance with platform, SRE, and AI teams using modern engineering practices.

Translate cutting-edge agent research into secure, production-grade runtime systems: prototype, benchmark, reproduce methods, build agent harnesses and evaluation/red-team workflows, secure agent toolchains, and partner across research, product, and engineering to integrate features into OpenShell.

As a Principal Software Engineer, you'll develop software systems for NVIDIA's rack-scale infrastructure, defining software architecture, collaborating across teams, mentoring engineers, and ensuring high-quality technical decisions in complex environments.

Design, develop, and operate massively distributed GPU cluster platforms for DGX Cloud. Build tooling to identify, diagnose, and remediate non‑performant GPU assets; ensure reliable, high‑performance production AI clusters; collaborate across teams; evaluate failures and improve services; work across frontend and backend stack.

Sell NVIDIA's networking portfolio to enterprise accounts, driving quota attainment by identifying, qualifying, and closing opportunities. Lead customer conversations across business and technical stakeholders, collaborate with solution architects and partners, and support solution development for AI and data center initiatives. Maintain pipeline, forecasting, and opportunity tracking in Salesforce while coordinating complex sales motions and building strategic customer relationships across Physical AI and Financial Services industries.

Own product marketing for NVIDIA Earth-2: define positioning and go-to-market strategy, write technically grounded content, align sales/partners/customers, create visual narratives, and coordinate across product, engineering, PR, and campaign teams to drive adoption in climate and weather communities.

Provide expert-level technical support for NVIDIA DGX compute/GPU platforms: triage and resolve hardware and software issues on multi-distro Linux, debug installations and interoperability, coordinate with engineering/marketing, create KB articles, and improve support processes. Interact directly with customers via phone/email/conference and apply AI tools for debugging and documentation.

Lead cross-stack adoption of modern toolchains, build and code-health practices, and performance workflows on NVIDIA CPU platforms. Integrate and validate compilers, linkers, cross-compilation, and CI; profile and optimize large codebases; build automation and playbooks; drive integration blockers to owners and communicate measurable before-and-after results to engineering teams and leadership.

Partner with university researchers to enable GPU-accelerated training and inference for foundation models, multimodal AI, reasoning systems, and agents. Advise on distributed training, memory/throughput optimization, reproducible workflows, and evaluation; build prototypes, translate lab feedback, and support adoption across NVIDIA platforms. Travel up to 20%.

As a Senior GPU Architect, you'll design and implement GPU profiling features, contribute to hardware models, and work with interdisciplinary teams to improve performance insights.

Design, deploy, and operate Kubernetes-based large-scale storage and data platforms. Build automation, tooling, telemetry, and observability. Troubleshoot distributed systems, support CI/CD and IaC, participate in on-call rotations, capacity planning, and launch readiness.

Apply machine learning to real-time graphics and interactive experiences: develop models from latest research, prototype applications, collaborate across teams, and productize neural graphics features for gaming and visualization.