Jobs at NVIDIA

Develop AI compiler solutions, optimize GPU programming, build training pipelines, collaborate on integrating AI policies, and conduct rigorous performance benchmarking.

Lead technical vision and strategy for next-generation graphics developer tools. Design and prototype tooling, drive cross-team initiatives, mentor engineers, engage external developers and partners, and deliver production-ready solutions that improve graphics development, debugging, profiling, and optimization workflows.

Lead and scale engineering teams to productionize NVIDIA's interactive world-model platform (OmniDreams, FlashDreams, AlpaSim). Drive architecture, CI/CD, performance, reliability, and developer experience; partner with research and product teams to translate research into scalable, production-ready capabilities and grow the open developer ecosystem.

The Solutions Architect will drive AI/ML networking solutions, collaborating with customers, sales, and engineering to develop and support critical infrastructure, while managing performance issues and feature requests.

Seeking a Machine Learning Compiler Engineer with expertise in compiler technologies, machine learning, and software engineering to innovate in development tools and AI solutions.

The role involves developing AI models for fault-tolerant quantum systems, conducting research in quantum computing, and collaborating with teams to integrate AI solutions into quantum hardware.

Design and build a scalable failure-attribution framework for heterogeneous CPU/GPU clusters: collect high-fidelity telemetry, implement low-overhead tracing, automate root-cause classification (hardware, software, environment), and enable proactive resiliency measures like migration or checkpointing.

Lead cross-functional programs to deliver on-prem and cloud-integrated DGX Cloud infrastructure: drive large-scale bare-metal fleet bring-up, automation, software integration, capacity operations, roadmap alignment, program governance, and metrics-driven operational reviews. Partner with NCPs and internal engineering, SRE, and data center teams to establish validation, bootstrapping, and maintenance workflows for production-ready clusters.

The Senior Memory Controller Verification Engineer will develop verification infrastructure, drive test plans, ensure coverage, and assist with post-silicon verification for NVIDIA's GPU Memory Subsystem.

Lead complex software program management at NVIDIA, focusing on cross-functional initiatives for AI software platforms, ensuring timely delivery and operational readiness.

Develop high-fidelity performance models for AI systems, prioritize features for software and hardware roadmaps, and evaluate the performance of GenAI workflows.

Design, build, and operate exascale distributed systems and orchestration for AI research. Implement storage and compute scheduling across multi-region clusters, improve reliability and observability, collaborate with security and platform teams, and translate researcher requirements into scalable, high-performance infrastructure.

Lead product definition and roadmap for AI factory resilience platform features (telemetry, health-checks, observability APIs, deployment experience). Drive developer experience, open-source community collaboration, cross-functional alignment, and partner with engineering on design, prioritization, and delivery.

The role involves collaborating on software products, tracking AI Infrastructure trends, supporting product marketing, and enhancing the NVIDIA ecosystem through feedback and coordination with engineering teams.

The Senior Software Engineer will optimize AI workloads using machine learning techniques for large scale GPU and CPU clusters, focusing on resource allocation, performance analysis, and collaboration on hardware/software teams.

Lead cross-disciplinary software architecture for next-generation CPUs and SoCs, collaborating with hardware, software, ASIC, verification, and platform teams. Influence hardware decisions via deep system software, OS/driver, and performance expertise, advise partners and senior management, and drive optimized solutions for AI and HPC workloads.

Design and build AI-native, high-performance CI/build/test/validation infrastructure for large Tegra C/C++ codebases. Optimize build graphs, caching, incremental builds, remote execution, device-lab testing, and integrate reasoning agents for autonomous triage, remediation, and self-healing workflows. Package reusable platform capabilities, drive adoption, and measure developer feedback loop improvements.

Lead development and maintenance of node provisioning, OS image build/packaging, and nodepool lifecycle for NVIDIA Kubernetes Engine. Build CAPI providers, BYON onboarding, hardened image pipelines with automated CVE remediation and compliance gating, and automated test suites. Troubleshoot node-layer failures at scale, collaborate with upstream Kubernetes/CAPI communities, and ensure reliable GPU-optimized node operations across large clusters.

Design, build, and maintain Kubernetes-native APIs and Go-based reconciliation controllers to provision and manage multi-cloud, GPU-accelerated ML/AI infrastructure. Contribute to IaC automation, CI/CD pipelines, testing, and cross-team integrations while collaborating on architecture and open-source projects.

Lead performance and scalability characterization of the DGX Cloud stack across Kubernetes and NVIDIA components. Design automated workload tests, build monitoring and CI/CD frameworks, triage large-scale Kubernetes issues, collaborate with researchers and upstream open-source communities, and present findings externally.