Jobs at NVIDIA

Supports NVIDIA’s Worldwide Field Organization by partnering with senior leaders on HR strategy, organizational design, workforce planning, talent management, performance management, compensation, employee relations, engagement, and leadership development. Advises managers and employees, analyzes workforce metrics, leads annual talent processes, and develops people solutions in a global, matrixed environment.

Advise on privacy law and data protection, review vendor contracts and DPAs, support third-party data sharing and transfer risk assessments, develop privacy review processes and playbooks, counsel business partners on privacy issues, monitor U.S. and international regulations, and assist with data governance guidelines and training.

Develop and optimize TensorRT/TensorRT-LLM inference software for GPUs. Perform performance analysis, tune deep-learning workloads, integrate research advances, collaborate across HW/SW teams, and deliver substantial technical features while publishing results.

Analyze and tune HPC applications to improve performance across CPU and multi-node GPU systems. Identify compiler and runtime optimization opportunities, port applications to different platforms, evaluate system and driver impacts, and collaborate with compiler and application engineering teams to implement performance improvements.

Own the full lifecycle of NVIDIA flagship events, including planning, budgeting, vendor management, programming, registration, attendee experience, on-site execution, executive communications, and post-event reporting. Coordinate cross-functional teams and agencies while managing multiple large-scale events simultaneously. Improve event formats and operational efficiency across a diverse portfolio, with approximately 60% domestic and international travel.

Develop and productize deep learning perception systems for autonomous driving, including traffic signal, sign, road marking, and construction object understanding. Lead end-to-end model development, data-driven improvement, simulation, augmentation, and deployment to embedded platforms. Optimize solutions for accuracy, safety, latency, robustness, and regional generalization while collaborating with data-labeling teams and contributing technical leadership.

Develop and scale simulation infrastructure for autonomous vehicle testing and validation across millions of virtual miles. Build metrics, test sets, and automation for KPI measurement, coverage, data quality, and performance. Collaborate with engineering, infrastructure, test, and release teams to resolve system-level issues and improve reliability. Mentor junior engineers and promote test-driven development. The role focuses on autonomous systems simulation, software engineering, data analysis, and large-scale validation.

Evaluates customer creditworthiness, recommends credit limits and payment terms, releases orders from credit hold, manages credit insurance and guarantees, monitors payment trends and overdue accounts, and partners with finance and sales teams to mitigate credit risk. The role also supports complex financial analysis, international trade compliance, process improvement, and efficient credit operations.

Own end-to-end verification of clocking and reset structures for automotive and datacenter ASICs. Develop test plans, automation, validation flows, coverage metrics, and execute tests to identify and resolve bugs. Apply SystemVerilog, UVM, C++, Perl, Python, formal verification, and profiling tools across block- and system-level platforms while collaborating with architecture, design, and distributed engineering teams.

Develop and integrate autonomous vehicle control software for trajectory tracking, model predictive control, state estimation, vehicle dynamics, and safety-critical systems. Build embedded C/C++ modules, simulation models, testing and benchmarking tools, calibration workflows, and automated verification suites. Integrate control functionality across vehicle platforms and troubleshoot actuation issues while contributing to system architecture, redundancy, and failure analysis.

Develop and ship behavior and trajectory planning software for L4 autonomous vehicles. Build optimization, search, and sampling-based planning algorithms that account for vehicle dynamics, safety, comfort, and traffic rules. Integrate planning with perception, prediction, mapping, localization, and controls. Create fallback behaviors, simulation benchmarks, evaluation automation, debugging tools, and regression testing. Support cross-platform vehicle integration and resolve on-road and simulation issues while contributing to safety validation and system architecture.

Develop and maintain scalable, resilient infrastructure software for large-scale AI, LLM, and GenAI training and inferencing. Diagnose failures across application and hardware layers, build distributed systems, design APIs, improve infrastructure efficiency, and establish reliability metrics. The role involves operating observability platforms and collaborating on NVIDIA AI platforms, GPUs, networking, deep learning frameworks, and datacenter-scale systems.

Lead hands-on storage software engineering for large-scale GPU clusters. Contribute to open-source parallel and distributed file systems, write and review production code, troubleshoot I/O, metadata, corruption, and recovery issues, validate performance and durability through scale testing, and establish configuration and operational standards. Collaborate with AI, SRE, networking, security, cloud, and storage teams while supporting high-performance storage across cloud, neocloud, and on-premises environments.

Develop and productize CUDA driver features for new NVIDIA GPU chips and systems, spanning pre-silicon simulation, silicon bring-up, and production deployment. Design, implement, debug, and test system software across multiple operating systems while collaborating with hardware and software teams. Contribute to CUDA APIs, programming models, kernel-mode development, operating-system interfaces, and forward-looking GPU architecture improvements.

Provide pre-sales technical expertise for NVIDIA Spectrum-X whitebox networking platforms. Guide customers through SONiC or custom network operating system bring-up, feature enablement, and debugging. Advise on SAI and the NVIDIA Spectrum SDK, troubleshoot issues across customer NOS and software layers, collaborate with R&D on fixes, and contribute customer requirements to product roadmaps.

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.

Drive procurement lifecycle automation, reporting, forecasting, governance, and process improvement for NVIDIA’s data center operations. Analyze procurement data to identify trends, exceptions, and risks; develop mitigation strategies; and collaborate with Finance, Engineering, Legal, vendors, and business stakeholders across a global matrixed organization.

Drive deployment, packaging, optimization, and customer adoption of NVIDIA Inference Microservices for enterprise AI workloads. Serve proprietary and open-source LLMs and VLMs across on-premises, cloud, and hybrid environments; optimize inference and reinforcement-learning rollout performance; evaluate models; deliver technical projects and demos; and provide customer support. Collaborate across research, engineering, infrastructure, product, and customer teams to expand AI solutions.

Develop production-grade integrations for NVIDIA Grove across AI frameworks including Dynamo, llm-d, Ray, and PyTorch. Build adapters, plugins, operators, runtime components, reference workflows, and developer tooling. Optimize distributed multi-node and multi-GPU training and inference, improve Kubernetes observability, define APIs, and troubleshoot containers, networking, scheduling, CUDA, and runtime issues. Collaborate with engineering teams and open-source communities to upstream changes and maintain compatibility across evolving frameworks.

Develop and troubleshoot networking system software for NVIDIA enterprise customers. Own InfiniBand and NVLink switch deployment issues, create features and tools, improve networking product reliability, analyze complex datacenter protocols, and collaborate with engineering teams to reproduce and resolve problems. The role combines customer-facing technical support with research and development across Mellanox Networking, DGX platforms, container orchestrators, and deep learning infrastructure.