Jobs at NVIDIA

Lead and scale an engineering team building GPU-accelerated deep learning inference frameworks. Drive strategy and roadmap, partner with compiler/libraries/research teams, oversee performance tuning and multi-GPU optimizations, mentor engineers, and deliver production-ready inference pipelines for LLM, multimodal, and generative AI across datacenter and edge platforms.

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.

Lead product definition, execution, and go-to-market for AI physics models, libraries, and frameworks. Collaborate with engineering, developer relations, marketing, and sales; present to internal and external audiences; prioritize projects; translate technical capabilities into developer and customer value.

Develop and evaluate machine learning models and datasets linking early drug discovery to clinical outcomes (perturbation prediction, translatability, patient stratification, digital twins). Design benchmarks for agentic systems, publish research and open-source code, collaborate with external and internal teams, and transition research into products and services.

Lead technical partnership with Alphabet teams to drive adoption and integration of NVIDIA libraries, models, and frameworks. Coordinate cross-functional engineering and GTM teams, resolve integration challenges, influence product roadmaps, and deliver joint solutions that accelerate partner workloads and developer adoption.

Design, implement, and optimize scalable high-performance dense linear algebra kernels for GPUs. Provide technical leadership, collaborate with product and partners, improve performance and quality, and contribute to library roadmaps and development practices.

Design and implement an LLVM-based JIT backend for NVIDIA GPUs, develop compiler passes and optimizations to meet performance and compile-time targets, collaborate with hardware and application teams to resolve issues, apply modern compiler technologies, and influence future GPU architectures.

Lead technology strategy and system architecture for next-generation wireless infrastructure. Apply AI/ML to RAN (L1/L2+) and transform core to agentic AI-based packet core. Prototype GPU-accelerated algorithms, engage partners and standards bodies, and drive performance optimizations and new edge applications (ISAC, SemCom, Physical AI).

The Senior HPC Architect role involves deploying large-scale GPU compute clusters, optimizing system administration, and collaborating with researchers on HPC and AI solutions.

Translate cutting-edge agent research into secure, production-grade runtime systems: prototype, benchmark, reproduce methods, build agent harnesses and evaluation/red-team workflows, secure agent toolchains, and partner across research, product, and engineering to integrate features into OpenShell.

As a Senior Solutions Architect, you will lead GPU-accelerated AI solutions development in healthcare, working alongside pharma companies to optimize simulations and robotic systems. You'll implement projects from concept to deployment while collaborating with various stakeholders.

As a Principal Software Engineer, you'll develop software systems for NVIDIA's rack-scale infrastructure, defining software architecture, collaborating across teams, mentoring engineers, and ensuring high-quality technical decisions in complex environments.

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.

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.

Lead and coordinate global software programs for deep learning training and inference frameworks. Drive cross-functional alignment, manage schedules, risks, and dependencies, implement release and open-source processes, define metrics, report status to senior management, and improve program execution across hardware, software, and infrastructure teams.

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.

Own product marketing for NVIDIA Earth-2: define positioning and go-to-market strategy, write technically grounded content, align sales/partners/customers, create visual narratives, and coordinate across product, engineering, PR, and campaign teams to drive adoption in climate and weather communities.

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.

Provide expert-level technical support for NVIDIA DGX compute/GPU platforms: triage and resolve hardware and software issues on multi-distro Linux, debug installations and interoperability, coordinate with engineering/marketing, create KB articles, and improve support processes. Interact directly with customers via phone/email/conference and apply AI tools for debugging and documentation.