Jobs at NVIDIA

Research and develop AI-native numerical algorithms that combine machine learning with scientific and engineering solvers. Build solver-in-the-loop pipelines, learned preconditioners, AI-guided multigrid methods, differentiable solver components, and hybrid ML/numerical methods. Evaluate convergence, stability, accuracy, robustness, failures, resource use, and performance. Collaborate across NVIDIA Research, GPU computing, simulation, engineering, semiconductor, and digital twin teams while shaping the applied research agenda.

Create and deliver developer-focused technical content and workshops that teach CUDA and NVIDIA GPU technologies. Evaluate new CUDA features, produce tutorials and blog posts, capture developer feedback to inform product decisions, and collaborate across product, engineering, and marketing teams to drive developer adoption.

Design and optimize high-performance C++/CUDA libraries for GPU-based data processing. Improve multi-node GPU communication (UcxExchange), optimize Presto GPU query performance, and lead enhancements across Presto, Velox, and cuDF projects for efficient DataFrame and database acceleration.

Drive the development and roadmap of HPC and AI/ML products for physics, plasma, high-energy-density simulations, and resilient materials discovery. Define features, requirements, positioning, and go-to-market strategy; collaborate with engineering and NVIDIA software teams; identify customers and partnerships; and represent products at workshops and conferences.

Build and lead development of NVIDIA’s open-source NeMo Core Platform for executing, evaluating, deploying, and optimizing agentic AI systems across local, Docker, Kubernetes, Slurm, on-premises, and air-gapped environments. Design platform APIs, plugins, SDKs, job orchestration, authentication, RBAC, observability, testing, and reliability features. Provide technical leadership through architecture and code reviews, mentoring, and ownership of complex cross-component problems.

Lead NVIDIA’s RL post-training frameworks strategy and engineering ecosystem across distributed training, inference, rollout, evaluation, orchestration, and NVIDIA platforms. Build and manage globally distributed teams, prioritize upstream and internal investments, establish benchmarks and execution metrics, and drive reliable open-source integrations. Partner with research, product, hardware, CUDA, networking, and external communities to scale reinforcement learning workloads across GPUs and heterogeneous systems.

Design and implement LLM-first APIs and documentation for CUDA-X math libraries. Develop tools, metrics, and processes to improve AI-agent code generation and transform library APIs into LLM-friendly formats. Collaborate with stakeholders on LLM-enhanced releases while prototyping solutions for code-generation platforms. Requires expertise in LLMs, fine-tuning, reinforcement learning, RAG, MCP, agent tooling, composable API design, and technical documentation.

Design and develop next-generation scale-up networking fabrics for AI data centers. Lead collaborative development across hardware, software, and applications to improve scalability, reliability, and robustness for large-model training. Create innovative concepts through production implementation, collaborate with cross-functional teams, and lead complex projects delivering large-scale networking and AI solutions.

Lead relationships with major research universities, advising institutional leaders, research computing teams, and principal investigators on NVIDIA technologies. Drive business growth, revenue forecasting, go-to-market strategy, platform adoption, and ecosystem enablement. Build partnerships across scientific disciplines, coordinate internal technical and product teams, communicate technology roadmaps, and develop expertise in emerging computational methods. Travel approximately 25% or more and support institutions in the Northeast or Mid-Atlantic regions.

Manage strategic state and local government accounts for NVIDIA’s AI and accelerated computing solutions. Own the full technology sales cycle, develop territory and account plans, build executive and technical relationships, collaborate with solution architects and channel partners, navigate public-sector procurement, forecast revenue, and achieve sales quotas. The role requires consultative selling to government organizations and travel of at least 25%.

Lead an engineering team responsible for reliability processes and platforms, including incident management, maintenance, on-call, issue management, and production readiness. Own the roadmap, technical direction, delivery, adoption, and continuous improvement of operational systems. Partner across infrastructure, product, and security teams; lead major incident response; hire and develop engineers; and apply automation, AI, and operational insights to improve service reliability and reduce manual work.

Develop scalable distributed storage management systems for NVIDIA’s large-scale engineering infrastructure. Collaborate with hardware and software teams to define features, optimize workflows, improve software design and testing, and deliver reliable storage solutions. The role requires Linux, storage systems, networked distributed systems, Perl, and Python expertise, with responsibility for architectural decisions balancing cost, performance, reliability, and scalability.

Develop and support NVIDIA CUDA Math Libraries across emerging and specialized platforms. Responsibilities include platform integration, functional qualification, defect triage, bug fixing, performance benchmarking, regression resolution, and building CI/CD qualification pipelines. The role requires high-performance numerical software development using C++ and Python, parallel computing, numerical methods, and software architecture, with preferred experience in CUDA, GPU computing, math libraries, cross-platform development, and embedded or safety-critical systems.

Lead NVIDIA’s Federal and SLED General Accounts sales team, managing Account Managers across government, higher education, and research customers. Develop go-to-market strategy, pipeline, demand generation, forecasting, revenue performance, and regional growth. Build executive relationships and coordinate technical, product, marketing, developer, and partner resources. The role requires extensive public-sector technology sales experience, leadership capability, knowledge of government procurement and buying cycles, and the ability to sell advanced AI, accelerated computing, and infrastructure solutions.

Develop and productize machine learning models for autonomous vehicle perception, including LiDAR and camera perception, sensor fusion, detection, classification, segmentation, and tracking. Build scalable data, training, metrics, and performance workflows, and implement efficient C++ code for the NVIDIA DRIVE AV platform. Collaborate across engineering teams, track advances in machine learning, and optimize models for real-time embedded inference.

Design and guide security architecture for NVIDIA networking firmware and embedded subsystems. Responsibilities include threat modeling, analyzing firmware and hardware interactions, defining lifecycle security features, translating threats into requirements, partnering with hardware architects, and driving secure firmware development practices such as memory-safe C, static analysis, and fuzzing. The role requires expertise in firmware security, authorization models, cryptography, secure boot, attestation, and embedded systems.

Build and operate security controls, telemetry, detections, and enforcement for NVIDIA’s large-scale EDA compute, storage, and networking infrastructure across on-premises and multi-cloud environments. Investigate threats, respond to incidents, automate secure configurations and compliance evidence, threat-model infrastructure, and manage security technology lifecycles. The role requires hands-on Linux, networking, programming, infrastructure security, and collaboration with infrastructure and security teams.

Builds secure AI and agentic cybersecurity tooling, transforming research prototypes into production-ready services. Responsibilities include developing backend services, APIs, CLI tools, automation, and integration patterns; defining MVPs for ambiguous security and identity challenges; collaborating across research, product, enterprise security, and platform teams; and applying offensive security and adversarial thinking to address AI-specific risks such as injection, tool misuse, unauthorized access, data exfiltration, and unsafe delegation.

Conduct novel research in AI security and cybersecurity, focusing on securing AI models, agents, systems, and infrastructure. Develop tools and processes to identify and remediate vulnerabilities, investigate adversarial machine learning, publish findings, and collaborate across NVIDIA. Present complex technical results to security, machine learning, and business audiences while contributing to secure software development and AI safety.

Develop and maintain automated integration and deployment for components in a Kubernetes-based software stack. Manage third-party dependencies, validate compatibility, maintain integration workflows, troubleshoot cross-component issues, and ensure compatibility with NVIDIA hardware. The role requires extensive Linux software development experience, Python proficiency, Kubernetes and container expertise, and collaboration with QA, CI/CD, development teams, and upstream projects.