Jobs at NVIDIA

Develop and optimize reinforcement learning algorithms and infrastructure for large language and multimodal models. Enable open-source model support in NVIDIA RL and post-training frameworks, collaborate with research and engineering teams, guide customers integrating NVIDIA technologies, and maintain reusable toolchains, experiment workflows, and documentation. The role requires strong PyTorch, distributed training, and experimentation skills, with preferred experience in RLHF, GRPO, DPO, GPU optimization, agentic AI, and multimodal reinforcement learning.

Serve as NVIDIA’s technical liaison with OEM server manufacturers, driving adoption of Arm-based data center CPUs. Architect and validate enterprise and AI server platforms, guide x86-to-Arm workload migration, tune performance, resolve CPU-memory-network integration issues, and collaborate with engineering, ISV, and open-source communities. Create technical content and present to technical and executive audiences. Support firmware, Linux, bootloader, hardware validation, benchmarking, and competitive performance analysis.

Oversee planning and execution of VLSI programs in Networking, ensuring alignment with corporate goals, while managing cross-functional teams and addressing risks.

The Senior Account Manager will drive the adoption and growth of NVIDIA GeForce products across Korean OEM partners, manage relationships, and develop new business opportunities to accelerate revenue growth.

Design and deploy AI applications for telecommunications networks, focusing on generative models and infrastructure improvements, while providing technical guidance and building enterprise solutions.

Designs and develops physical implementation for high-speed networking chips, optimizing power, area, and performance. Responsibilities include RTL-to-GDSII flows, synthesis, power and clock distribution, place and route, timing closure, power and noise analysis, physical verification, and resolving timing and congestion challenges. The engineer also contributes to flow development and integration while applying LVS and DRC verification methodologies using industry-standard EDA tools such as Synopsys and Cadence.

Perform physical design for chip blocks and top-level designs from RTL through GDS. Responsibilities include synthesis, power and clock distribution, place and route, timing closure, power and noise analysis, physical verification, and resolving timing and congestion challenges. The engineer will contribute to flow development and serve as the technical leader and focal point for partition- or unit-level physical design.

Develop and validate digital test content (ATPG, LBIST, MBIST) for network ICs; run simulation/emulation/silicon validation flows, analyze failures, debug patterns, collaborate with DFT and test teams, support tester bring-up and pattern integration, and improve coverage and pattern generation efficiency to support yield and product quality.

Develop embedded real-time systems software for autonomous vehicle safety services. Optimize solutions for performance and compliance while collaborating with multifaceted teams to integrate technologies into NVIDIA's self-driving platform.

This role involves developing high-performance software for autonomous vehicles, focusing on sensor drivers, data processing, and vehicle interface integration, while adhering to quality standards.

Develop system requirements, performance budgets, test strategies, metrics, and verification processes for L3/L4 autonomous driving products. Analyze safety-critical systems, datasets, and software architectures while coordinating vehicle and component testing. Partner with engineering, safety, validation, analytics, and product teams to ensure functional and safety compliance. Apply SOTIF, ISO 26262, architectural trade-off analysis, and validation strategies for deep learning and modular autonomous-driving systems.

Operate and improve NVIDIA’s hybrid Active Directory and Entra ID environment. Implement Zero Trust capabilities including SSO, MFA, conditional access, and privileged access. Build automation for identity lifecycle, access requests, directory hygiene, service accounts, workload identities, and AI agents. Apply AI-assisted telemetry, anomaly detection, health checks, and diagnostics to improve infrastructure stability and user self-service. Collaborate with global IT, cybersecurity, and business technology teams on the IAM roadmap.

Leads end-to-end development and execution of autonomous driving software features across multiple engineering teams. Defines roadmaps, schedules, release content, quality metrics, and program tracking while resolving technical challenges affecting scope, schedule, and quality. Communicates program status to management and cross-functional teams. Requires strong experience in complex software engineering programs, automotive, robotics, or AI, with leadership across global organizations.

As an EDA Workflow Optimization Engineer, you will optimize and enhance workflows for chip development, working with teams to investigate, debug, and innovate tools for efficient engineering processes.

Design, develop, and bring up embedded software for autonomous vehicle hardware, including cameras, radar, and lidar. Integrate and debug hardware and software across vehicle subsystems, support DRIVE platform customization for customers, improve platform reliability, and resolve complex issues from application through kernel level. Work across Linux, QNX RTOS, hypervisor, and ARM-based SoC environments while collaborating with software, hardware, systems, and customer teams.

Lead development of large-scale reinforcement learning frameworks and simulation pipelines for autonomous vehicle foundation models. Design closed-loop simulation, optimize reward functions and simulation quality, build scalable data-processing workflows, and improve training reliability on GPU clusters. Develop monitoring and debugging tools while collaborating with platform, product, and research teams to integrate advanced model architectures and productize machine learning solutions.

Lead end-to-end Tegra diagnostic software planning and execution across the SoC silicon lifecycle, including pre-silicon development and validation, silicon and platform bring-up, and productization. Coordinate cross-functional teams, schedules, milestones, dependencies, technical activities, and software lifecycle deliverables across global locations. Partner with product design teams to prioritize and implement custom bring-up requirements for embedded, automotive, robotics, autonomous machine, and data center platforms.

Designs and optimizes multi-agent AI systems for NVIDIA’s CUDA ecosystem, including agent abstractions, GPU-centric runtimes, and compiler- or runtime-driven solutions. Develops evaluation frameworks for performance, quality, and developer productivity. Collaborates across hardware, compilers, kernels, libraries, frameworks, distributed training, inference, and model teams. Requires strong C/C++, Python, GPU programming, CUDA, deep learning frameworks, and modern inference-stack experience.

Leads networking circuit board and system manufacturing at NVIDIA’s Guadalajara factory. Responsibilities include NPI readiness, factory qualification, production ramp support, yield and process improvement, root-cause analysis, reliability and test issue resolution, manufacturing data analysis, DFM/DFT collaboration, engineering change implementation, and coordination among global engineering teams, contract manufacturers, suppliers, and quality organizations.

Leads technical, process, and application initiatives supporting supply chain, planning, manufacturing, quality, and service operations. Responsibilities include defining requirements and roadmaps, managing scope, risks, resources, schedules, and multiple concurrent projects; improving and reengineering business processes; coordinating engineering teams; developing business use cases; and communicating decisions and progress across cross-functional stakeholders. The role requires supply chain expertise, technical program leadership, and experience with IT applications, infrastructure, data analytics, and Agile Scrum delivery.