Jobs at NVIDIA

Build and optimize local LLM inference and agent runtimes for Windows and GeForce RTX GPUs. Profile and reduce latency/memory, integrate CUDA/TensorRT pipelines, implement sandboxed security and privacy routers, and harden multi-agent orchestration for constrained consumer hardware while collaborating across research, driver, and open-source teams.

Own technical integration and adoption of NVIDIA libraries, models, and frameworks across Microsoft; coordinate product and engineering teams, drive developer advocacy, resolve technical challenges, influence partner roadmaps, and deliver joint GTM solutions and early wins.

Develop, debug, and harden kernel-mode GPU and SoC system software across design, emulation, bringup, and production. Focus on performance, power optimization, robustness, and cross-team collaboration for datacenter, desktop, workstation, and console platforms.

As a Principal System Architect, you'll define GPU architecture, lead cross-functional teams, evaluate power and performance, and mentor junior engineers in SoC development.

The role involves developing AI-driven compiler technologies, optimizing workflows, integrating learning systems, and collaborating on next-gen compiler capabilities for improved performance.

The role involves developing AI models for autonomous vehicles, focusing on LLMs and VLMs, and ensuring seamless deployment and integration in production environments.

Provide pre-sales technical support and solutions architecture for NVIDIA networking (Spectrum-X) in AI/HPC deployments. Develop proofs-of-concept, debug complex firmware and networking issues, collaborate with customers and R&D, and help define product requirements and features.

Lead research and prototype novel real-time path tracing, sampling, lighting, and denoising methods; implement prototypes, publish, transfer ideas to product teams, and mentor researchers and interns.

Lead and grow a technical team responsible for verifying AI compilers for next-generation workloads. Define formal verification requirements, drive AI-assisted and compiler-aware verification (fuzzing, differential testing, symbolic reasoning, formal methods), and partner with compiler, CUDA, runtime, and ML framework teams to build scalable verification infrastructure and improve production confidence.

Lead strategy, forecasting, and revenue growth for assigned US Tier 1/2 telecom accounts. Own customer relationships, build strategic partnerships, drive go-to-market plans with partners and OEMs, and evangelize NVIDIA platforms and AI solutions. Collaborate cross-functionally with solution architects, product marketing, and developer relations. Travel as needed.

Lead development and optimization of local AI agent frameworks and runtimes on Windows and GeForce RTX hardware. Ensure secure, policy-driven sandboxing, privacy routing, and efficient local LLM inference. Collaborate with AI research, driver teams, and open-source communities while mentoring engineers and delivering production-ready integrations for consumer PCs.

Lead product launch strategy and go-to-market plans for NVIDIA data center and GPU-accelerated cloud offerings. Drive messaging, partner launches with cloud providers, create marketing content, enable sales and channel partners, and collaborate with product, engineering, and campaign teams to communicate the platform value proposition.

Lead AI software resiliency development, implementing features for reliability and fault tolerance in large-scale AI supercomputers, requiring hands-on coding and collaboration.

Seeking a Senior Engineer in Deep Learning and Computer Vision to develop machine learning models for autonomous vehicles, utilizing NVIDIA's GPUs and collaborating with researchers.

Serve as a trusted technical advisor to NVIDIA Cloud Partners to design, integrate, and deploy large-scale GPU AI/HPC cloud infrastructure. Lead PoCs and production deployments of models, frameworks, and software stacks, run technical workshops and presentations, collaborate with sales, engineering, and product teams, and drive customer adoption and consumption.

Lead performance characterization and optimization of large-scale AI workloads across GPUs, networking, storage, and software. Design and run benchmarks and profiling, diagnose bottlenecks, define evaluation methodologies and metrics, produce actionable optimization plans, and collaborate with DL engineers, platform teams, and GPU architects to deliver improvements.

Lead development of local AI agent frameworks and optimized runtimes on Windows for GeForce RTX PCs. Ensure secure, sandboxed execution and privacy-aware inference routing, collaborate with AI research and driver teams, mentor engineers, and produce production-ready code for autonomous, always-on desktop AI assistants.

Lead development of local AI agent frameworks and runtimes on Windows for GeForce RTX PCs. Optimize GPU-accelerated LLM inference, enforce policy-based privacy and sandboxing, collaborate with research, driver, and open-source communities, mentor engineers, and produce production-ready code and deployment guidelines.

Design, build, and maintain SDKs and CLIs for cloud GPU services; collaborate with cross-functional teams to define requirements; implement RESTful services, ensure high-quality code with unit and integration tests, drive performance tuning, test automation, and document/support software.

Lead development of local AI agent frameworks and runtimes on Windows and GeForce RTX GPUs. Optimize agent inference, sandboxing, and privacy/security policies, collaborate with AI research and driver teams, mentor engineers, and deliver production-ready, local autonomous agent systems.