Jobs at NVIDIA

Lead a software engineering team developing foundational libraries and tools for agentic applications. Responsibilities include researching agent development patterns, identifying architecture gaps, building evaluation and benchmarking systems, integrating RAG, vector databases, data pipelines, and GPU-optimized workflows, and coordinating development from ideation through deployment. The role also involves mentoring engineers, defining platform roadmaps, partnering cross-functionally, scaling agent library adoption, and delivering reliable, governed enterprise-grade agent systems.

Designs and builds PyTorch components optimized for systems ranging from thousands to hundreds of thousands of GPUs. Collaborates with hardware and software teams to improve GPU performance, develops production AI solutions for enterprise customers, and supports internal AI research tools. The role requires deep learning expertise, C++ proficiency, systems software knowledge, distributed and parallel programming experience, and large-scale software development skills.

Develop and support NVIDIA networking technologies for AI clusters, including InfiniBand, NVLink, and Spectrum-X. Reproduce and root-cause complex customer issues down to source code, collaborate with R&D on fixes, analyze high-performance network metrics, and create diagnostic tools. The role requires extensive C and Python development in Linux and embedded systems, networking expertise, customer support experience, and strong communication skills. Occasional weekend or holiday support may be required.

Lead relationships with major research universities, advising institutional leaders, research computing teams, and principal investigators on NVIDIA technologies. Drive business growth, revenue forecasting, go-to-market strategy, platform adoption, and ecosystem enablement. Build partnerships across scientific disciplines, coordinate internal technical and product teams, communicate technology roadmaps, and develop expertise in emerging computational methods. Travel approximately 25% or more and support institutions in the Northeast or Mid-Atlantic regions.

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.

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.

Own the roadmap and delivery of AI infrastructure resilience platform features, including telemetry, health checks, attribution, observability APIs, and deployment experiences. Translate developer needs into product strategy, collaborate with engineering on architecture and execution, engage open-source contributors through GitHub, and align product, marketing, field, and engineering teams. The role requires deep technical expertise in AI infrastructure, Kubernetes, developer platforms, or related systems, plus extensive product management or engineering experience.

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 NVIDIA’s US Medtech Account Managers and drives enterprise sales growth across named manufacturing accounts. Responsibilities include demand generation, forecasting, executive relationship development, channel and partner collaboration, revenue target attainment, and coordination with internal teams to address customer needs. The role focuses on selling NVIDIA hardware and software for AI and accelerated computing in healthcare and medtech markets.

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.

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.

Leads technical developer-relations strategy for NVIDIA platforms across capital markets. Advises quants, traders, developers, financial institutions, ISVs, and cloud providers; demonstrates CUDA, CUDA-X, AI frameworks, and accelerated computing solutions; supports partner onboarding and integrations; develops benchmarks, sample code, and reference implementations; gathers ecosystem feedback; influences product roadmaps; and represents NVIDIA at industry events, workshops, and conferences.

Lead founding AI Safety & Security Engineering team, define harness architecture and engineering standards, drive Find/Validate/Patch roadmap, mentor engineers, and translate research into production-grade, secure ML systems.

Develop and formalize methods to validate reported vulnerabilities and generate/verify safe patches for internal NVIDIA targets. Define correctness, regression, and revalidation standards; produce repeatable, auditable processes and evidence. Apply AI-assisted workflows cautiously, perform bounded applied research on vulnerability classes, and document findings for independent security review.

Supports and root-causes complex customer issues involving NVIDIA networking and AI cluster technologies. Develops bug fixes, workarounds, diagnostic tools, and performance-tuning recommendations in collaboration with R&D and support teams. Works with InfiniBand, NVLink, Spectrum-X, and high-performance lossless networks, analyzing logs, traces, metrics, and source code. Provides customer support and may work occasional weekends or holidays.

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.

Designs and deploys large-scale AI and HPC GPU cloud infrastructure for NVIDIA Cloud Partners. Serves as the primary technical advisor across solution design, development, integration, production, debugging, and customer adoption. Builds proof-of-concepts, integrates AI software stacks, libraries, frameworks, and models, and delivers technical presentations and workshops. Partners with engineering, product, sales, and business teams to secure opportunities, guide product strategy, and support complex customer engagements throughout the lifecycle.

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.

Develop and deploy deep learning models and petabyte-scale multimodal data curation pipelines for foundation-model training. Responsibilities include document extraction, OCR, layout and table analysis, deduplication, distributed processing, dataset and metric development, experimentation, production scaling, and deployment through NVIDIA Inference Microservices. The role also involves publishing research, creating technical documentation, communicating findings, and mentoring team members.

Lead architecture for cloud networking and security solutions involving DPUs and NICs. Design end-to-end systems from application to hardware, define architecture specifications, evaluate technologies, develop proof-of-concept prototypes, and collaborate with global software, hardware, product, engineering, and customer teams. The role requires strong C and Python development, Linux, networking protocols, cloud technologies, and systems architecture expertise.