Jobs at NVIDIA

Develop and optimize deep learning compiler infrastructure for high-performance inference across GPUs, data centers, devices, automotive, and robotics. Responsibilities include analyzing neural networks, creating compiler optimization algorithms, defining APIs, improving performance and memory efficiency, implementing compiler techniques, and collaborating with software framework and hardware architecture teams. The role also involves software design, debugging, performance analysis, testing, and independently leading development efforts.

Leads the architecture and development of enterprise infrastructure automation and configuration management platforms across compute, storage, networking, data centers, cloud, and hybrid environments. Defines long-term technical strategy, builds secure and scalable integrations, applies AI to configuration intelligence and remediation, and remains hands-on with software development. Mentors senior engineers, establishes engineering standards, drives enterprise adoption, and partners with leadership on technical roadmaps and business outcomes.

Build the developer ecosystem for NVIDIA Dynamo, an open-source distributed AI inference framework. Responsibilities include creating demos, sample code, documentation, videos, technical talks, workshops, and office hours; collaborating with inference projects and open-source maintainers; troubleshooting deployments; gathering developer feedback; influencing engineering and product priorities; and communicating progress externally. The role requires hands-on experience with model serving, Kubernetes, distributed inference, performance optimization, and developer or partner programs.

Provide advanced technical support for NVIDIA Ethernet switching and AI infrastructure products. Troubleshoot Linux, server, hardware, operating system, networking, installation, interoperability, and operational issues for enterprise customers. Analyze protocols and packet captures, lead critical issue resolution, collaborate with Engineering, Marketing, and Support, and develop support methodologies and documentation. The role focuses on data center, HPC, networking, and AI infrastructure environments.

Provides advanced software and customer support for NVIDIA networking technologies used in AI clusters. Reproduces and root-causes complex field issues to the source-code level, develops fixes and workarounds with R&D, analyzes high-performance network metrics, builds diagnostic tools, and supports technologies including InfiniBand, NVLink, and Spectrum-X. The role requires extensive C or Python experience in Linux or embedded systems, networking expertise, and strong customer communication.

Provides advanced technical support for NVIDIA networking and AI infrastructure products, including InfiniBand, NVLink, Ethernet, GPUs, Linux, and HPC environments. Troubleshoots customer issues across installation, operation, maintenance, interoperability, and performance. Responds through multiple support channels, collaborates with Engineering and Marketing, develops support methodologies, documents solutions, and improves internal support processes. Requires extensive networking, Linux administration, virtualization, containerization, cloud infrastructure, packet analysis, and customer debugging experience.

Provides advanced technical support for NVIDIA InfiniBand, NVLink, GPU, networking, and HPC infrastructure products. Responsibilities include troubleshooting customer issues across installation, operations, maintenance, interoperability, Linux environments, and performance; responding through multiple channels; collaborating with engineering and marketing; and documenting support methodologies. The role requires deep networking, Linux administration, virtualization, cloud, containerization, and HPC expertise, along with strong communication and customer problem-solving skills.

Design, develop, test, and optimize system software for next-generation ARM-based SoCs across pre- and post-silicon phases. Analyze architectural bottlenecks, workload performance, and performance-per-watt tradeoffs; develop hardware/software policies and optimization strategies. Lead performance analysis, competitive benchmarking, architecture documentation, and adoption of improvements across NVIDIA software products and open-source SDKs.

Build and operate NVIDIA’s serverless AI inference platform across globally distributed, GPU-backed Kubernetes clusters. Lead feature implementation, testing, performance optimization, reliability improvements, customer issue resolution, reference architecture development, and technology evaluation. Mentor engineering teams on scalable AI/ML workloads and contribute to product requirements and roadmap development.

Develop and optimize NVIDIA’s proprietary C++ GPU compiler backend. Responsibilities include designing compiler passes, code-generation optimizations, and high-performance GPU machine instructions while collaborating with compiler, hardware, and application teams. The role supports graphics and compute technologies and contributes to deep-learning compiler capabilities. Candidates need 3+ years of compiler code-generation experience, strong C++ skills, and solid software engineering fundamentals. LLVM code-generation expertise and graphics or CUDA application experience are preferred.

Designs and implements application security protections for APIs, services, developer tools, and data platforms supporting autonomous vehicle and software infrastructure. Responsibilities include threat modeling, code and architecture reviews, authentication and authorization controls, secure handling of credentials and sensitive data, CI/CD security integration, vulnerability remediation, and reusable security tooling. The role partners with software, infrastructure, and corporate security teams to prioritize and measure security improvements.

Provide technical support to NVIDIA Enterprise customers by triaging and resolving issues, working with engineering on reproductions/logs, applying AI/ML and agentic AI to build support tools and products, documenting interactions and knowledge, and owning cases to resolution (occasional weekend/holiday support).

Support NVIDIA enterprise customers using GPU-accelerated platforms, triage hardware and AI/ML workload issues, collaborate with engineering teams, and drive cases through resolution. Develop support tools and products using AI, document customer interactions, and troubleshoot Linux-based multi-GPU systems, firmware, kernels, drivers, containers, and distributed workloads. The role also requires customer communication, technical analysis, and occasional weekend or holiday support.

Develop Rust-based, GPU-accelerated data processing frameworks and agentic retrieval pipelines for multimodal LLM applications. Build systems for dataset indexing, querying, deduplication, filtering, classification, benchmarking, and optimization. Collaborate with AI researchers and cross-functional teams to develop production tools, prototypes, and roadmaps. Apply prompt engineering, fine-tuning, profiling, and micro-optimization while supporting containerized cloud infrastructure, continuous delivery, peer reviews, and Gen-AI productivity products.

Leads the Kubernetes Runtime Engineering team responsible for tenant cluster configuration, container runtime reliability, GPU workload infrastructure, networking, storage, security, and multi-tenancy isolation. Manages engineers, drives architecture for fleet-scale cluster lifecycle operations, builds AICR lifecycle tooling, establishes hardening standards, partners across platform and cybersecurity teams, and represents runtime engineering in architecture, roadmap, customer, and open-source initiatives.

Lead the optimization and performance analysis of distributed training and inference workloads on NVIDIA GPU platforms, with responsibilities including debugging, benchmarking, and ensuring reliability of large-scale AI systems.

Lead development and optimization of local AI agent frameworks and runtimes for Windows PCs with NVIDIA GeForce RTX GPUs. Build secure, sandboxed, privacy-focused agent execution and inference systems, collaborate with AI research, driver, and open-source teams, and establish deployment guidelines. Mentor engineers while delivering production-ready code using Windows systems programming, C++, Python, LLM inference pipelines, GPU acceleration, and agent orchestration frameworks.

Lead security engineering for RTOS, hypervisor, and embedded virtualization: design and review defenses, collaborate on CPU/SoC security, perform threat modeling and automotive TARA, review low-level C/C++ code, build fuzzing and test infrastructure, assess vulnerabilities, and drive security fixes and verification toward automotive safety and security standards.

Design, build, and maintain infrastructure software for large-scale AI training and inference. Improve efficiency, resiliency, availability, observability, and reliability across distributed AI systems. Develop APIs and tools, analyze failures from application through hardware levels, define reliability metrics, and support NVIDIA AI platforms. The role requires expertise in distributed systems, monitoring, debugging, software engineering practices, and AI infrastructure, with preferred experience in large clusters, RDMA stacks, telemetry, and deep learning frameworks.

Design, develop, test, and deliver GPU client firmware and BIOS/UEFI solutions for x86 and ARM platforms. Collaborate with hardware, security, QA, ODM, and vendor teams throughout product development. Build automated unit tests, analyze code quality and performance, support platform-level interfaces, and influence hardware architecture. The role requires strong C/C++, scripting, debugging, and embedded operating-system expertise, with responsibility for end-to-end product delivery.