Jobs at NVIDIA

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.

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.

Lead cross-functional compiler software programs from concept to delivery, coordinating global teams, tracking risks and schedules, and improving development processes. Present status to executives, drive Agile and Waterfall projects, resolve technical/resource conflicts, and implement process and tool automation to ensure timely product releases.

Design, develop, integrate, test, and productize perception software for autonomous driving systems. Responsibilities include implementing robust and reusable software components, deploying deep neural network models to embedded real-time platforms, developing verification plans, debugging systems, maintaining documentation, and analyzing real or synthetic benchmark data. The role requires close collaboration with perception engineers and researchers throughout the software development lifecycle.

Design and build intelligent compiler systems, compiler-oriented agents, code transformation workflows, and numerical verification infrastructure. Develop systems for low-level IR reasoning, code generation, optimization, differential testing, regression detection, and correctness validation across GPU-centric workloads. Collaborate with compiler, CUDA, runtime, library, and hardware teams to integrate capabilities into NVIDIA products while optimizing performance, scalability, and numerical correctness.

Own end-to-end software integration across firmware, BSP, drivers, GPU software, services, and release systems. Lead system-level debugging of boot failures, crashes, resets, power transitions, performance regressions, and update issues. Analyze logs, traces, crash dumps, and hardware diagnostics to identify root causes, automate failure classification, integrate partner software, and coordinate cross-functional resolution across firmware, kernel, hardware, architecture, and OS teams.

Develop production tools, desktop applications, DCC integrations, and real-time workflow systems for Lightspeed Studios. Partner with artists and engineers to improve content pipelines, usability, reliability, performance, and automation. Build Python and Qt/PySide solutions, integrate applications such as Maya and Houdini with Unreal Engine, and explore agentic AI for content creation. The role requires extensive tools or pipeline engineering experience in games, VFX, animation, or real-time production.

Lead software architecture for DriveOS, decomposing requirements and integrating designs across system software, virtualization, graphics, compute, safety, security, sensor processing, and networking. Provide technical direction, resolve system-level issues, improve development processes, and communicate architecture decisions. Collaborate with software and hardware stakeholders to influence SoC and GPU designs for safe, reliable autonomous vehicle systems.

Develop compiler optimization algorithms for deep learning workloads, optimizing JAX and OpenXLA performance on NVIDIA GPUs. Design graph partitioning, tensor sharding, code generation, and performance-tuning solutions for distributed training and inference. Collaborate with deep learning framework and GPU hardware teams, implement user-facing JAX features, and contribute to production-grade software using MLIR, LLVM, and OpenAI Triton.

The role involves performing data analysis for datacenter applications, using machine learning for GPU systems, and providing insights on trends to enhance workflows.

Lead architecture for cloud-networking and design system architectures for DPUs and NICs. Build end-to-end solutions from application to hardware, write architecture specifications, evaluate new technologies, develop POCs, and collaborate globally with SW/HW teams and customers.

Leads architecture for cloud networking, security, DPU, and NIC solutions. Designs end-to-end systems spanning applications and hardware, researches technologies, develops proof-of-concept prototypes, writes architecture specifications, and collaborates with global software, hardware, product, engineering, and customer teams. Requires strong C, Python, Linux, networking, cloud, system architecture, and software development experience, with DPDK, OVS, OVN, container, Kubernetes, and data center expertise preferred.

Develop GPU-accelerated, PyTorch-based machine learning solutions, including Graph Neural Networks, foundation models, and ensemble models. Build and optimize distributed training and inference workflows, support CUDA-X integrations, improve high-performance code, collaborate with customers and cross-functional teams, and provide technical leadership and mentorship.

Leads multifunctional initiatives defining safety architectures and requirements for autonomous vehicles. Develops safety concepts and analysis supporting continuous software delivery, improves safety metrics and analysis tools, evaluates safety data, and communicates readiness results. Collaborates with perception, AI, motion planning, and architecture experts to ensure DRIVE products meet rigorous safety standards.

Lead technical and strategic partnerships with quantum processing unit builders and federal research organizations. Develop joint roadmaps integrating QPUs with NVIDIA GPUs, networking, and quantum software including CUDA-Q, cuQuantum, CUDA-Q QEC, and NVQLink. Coordinate cross-functional engineering and product teams, create reference architectures, run technical workshops and training, present at conferences, and advise senior scientific leaders on QPU-GPU integration, real-time control, decoding latency, and fault-tolerant quantum computing.

Leads NVIDIA’s Infrastructure Security Engineering organization for EDA clusters. Builds and operates security controls across compute, storage, and networking in on-premises and multi-cloud environments while preserving performance for long-running workloads. Owns the technical roadmap, security technology lifecycle, automation, instrumentation, compliance evidence, and cross-functional execution. Develops secure-by-default infrastructure and leads teams responsible for scalable enforcement, observability, mitigation, and security outcomes.

Leads multiple concurrent quantum computing R&D programs, translating research roadmaps into measurable execution plans and scalable technologies. Partners across applied research, engineering, product teams, and external organizations to prioritize work, manage risks, solve technical challenges, and advance quantum technologies toward fault-tolerant computing. Requires strong technical understanding of quantum hardware and software, exceptional communication, and extensive experience managing complex cross-functional technology initiatives.

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.

Operate and improve large-scale Kubernetes and GPU clusters across public and private clouds. Build automation, observability, capacity-management, and reliability systems; define SLOs and SLIs; support production launches; lead incident triage and root-cause analysis; conduct blameless postmortems; and participate in on-call support for AI workloads.