Jobs at NVIDIA

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%.

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.

Design, build, and operate core cloud-native infrastructure services for NVIDIA DGX Cloud. Develop scalable, secure microservices and automation for provisioning and lifecycle management, integrate internal and external platforms, and implement observability and security to ensure reliable, global production operations.

Build and maintain network automation and orchestration software for NVIDIA GPU Cloud and SuperPod deployments. Design/configure centralized configuration management, integrate cloud provider APIs, implement data pipelines and streaming telemetry, and automate lifecycle operations from procurement to deployment. Collaborate with infrastructure experts to deliver zero-touch, production-grade network provisioning, monitoring, and management tools.

Leads a distributed team securing NVIDIA DGX Cloud’s large-scale GPU infrastructure. Responsibilities include hiring and developing engineers, setting strategy for security control planes and foundational services, delivering measurable risk reduction, maintaining engineering standards, communicating security risks to executives, and collaborating with platform, SRE, networking, and security teams. The role requires strong engineering management, infrastructure or security expertise, cloud-native fluency, and influence across partner organizations.

Partners with customers and internal teams to design, deploy, and optimize generative AI and LLM inference solutions. Develops proofs of concept, supports NVIDIA technology adoption, analyzes AI workload performance and power efficiency on Kubernetes, and advises developers, researchers, and data scientists. The role requires expertise in deep learning frameworks, GPU orchestration, MLOps, containerization, observability, and LLM inference, with occasional travel to conferences and customers.

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.

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.

The role involves owning physics models, defining manufacturing specifications, and validating designs to ensure product quality and performance. The architect will utilize first-principles thinking and AI to improve workflows and drive alignment across engineering teams.

Lead and grow an engineering team building GPU‑accelerated deep learning inference frameworks. Drive roadmap, collaborate with compiler/libraries/research teams, optimize LLM and multimodal model performance, and oversee multi‑GPU communications and production deployment of inference software.

Lead hardware-software co-design for LLM inference: drive and document architecture, build executable C++ hardware models, optimize performance with good algorithms and parallelism, diagnose cross-team chip and subsystem issues, and automate workflows to improve efficiency.

Define high-level SoC subsystem and I/O architecture for LPU products, translate product requirements into architecture and IP specifications, build and validate functional/architectural models (C++, SystemC, Python), and drive tradeoffs across performance, power, reliability, and security while coordinating with IP, firmware, software, RTL, verification, and post-silicon teams.

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.

Analyze and optimize HPC application performance across CPU and multi-node multi-GPU systems by driving compiler and library improvements. Port and tune applications, evaluate compiler/runtime/driver impacts, and collaborate with compiler development and application engineering teams to deliver performance enhancements.

As a Senior Solutions Architect, you will assist customers in building solutions with NVIDIA's AI technology, focusing on Generative AI and Large Language Models while collaborating across teams for performance analysis.

Lead architecture, deployment, and operation of a hybrid HPC–quantum platform. Manage Linux-based GPU clusters, QPU hardware, schedulers (Slurm), networking, storage, and vendor integrations. Enable researchers by optimizing, benchmarking, and documenting hybrid workflows and supporting application performance and orchestration across classical and quantum systems.

Design, build, and optimize GPU‑accelerated inference software and open‑source frameworks (vLLM, SGLang, FlashInfer). Improve performance and scale for LLMs and generative models across NVIDIA GPUs and edge SoCs using CUDA, CUTLASS, Triton, NCCL and profiling tools. Collaborate across teams to enable efficient model serving and deployment.

The Senior Formal Verification Engineer will verify sophisticated ASIC builds using formal methods, collaborate with architecture teams, develop verification environments, and support silicon debug.

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.