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Define verification scope, develop and drive testplans (functional and performance), and build scalable UVM-based infrastructure; leverage agentic AI to automate repetitive tasks, accelerate debug, and sharpen team efficiency.
You will compose and build the architecture behind next-generation self-driving vehicle technology. Work on Prediction, Decision, Planning and Control architecture. Have exposure to Classical safety stack. Build robust system-level safety and fallback strategies.
Use internally developed tools and industry standard pre-silicon gate-level and RTL power analysis tools, to help improve product power efficiency. Use artificial intelligence to devliver RTL and/or Architecture Power optimization solutions.
Lead and grow a team of highly skilled engineers. Drive development of NvSci technology for next generation automotive and other Embedded applications. Apply and invent processes to ensure high software quality and reliable schedules.
Own strategy, roadmap, governance, and full delivery of NVIDIA’s global technology, business architecture, prioritization, investment recommendations, adoption, and tangible value achievement.
Own key engagements with our fast-paced developer ecosystem partners, optimizing their applications to deliver outstanding end-to-end performance on RTX and DGX SoCs.
Understand, analyze, profile, and optimize AI training workloads on state-of-the-art hardware and software platforms. Identifying performance bottlenecks of AI training on GPUs, prioritizing and then solving problems across the key AI training workloads.
You'll get involved with making key architectural decisions that will have a lasting impact on our software. In this role you'll have the exciting opportunity to figure out how to intersect the GPU and Linux kernel roadmaps to ensure NVIDIA's future success! .
Act as the lead engineer for developing the agent frameworks natively on Windows environments. You will build the technical roadmap to bring always-on, self-evolving AI assistants to GeForce RTX PCs and laptops.
You will partner across design, validation, software, and manufacturing to keep the program’s technical narrative clear and on track. Day to day, you will: Serve as the single technical point of contact for multi-functional decisions, issues, and trade-offs.
Become a member of an a gile and multi-functional EDA software development team. Participates in the development of tools that enable industry leading PCB electrical design, physical layout, and SI capabilities, for our system designers throughout the company.
Sensitive Information Protection & Remediation. Own the roadmap for sensitive-information detection and remediation — partnering with Finance, Legal, and Security to define classification models, remediation workflows, and reporting that leadership can trust.
Lead and code end-to-end features that power a seamless, AI-rich employee experience across web and native mobile apps. Architect highly performant services and UIs that surface personalized content, knowledge, and search results to a global workforce.
Coordinate all aspects of GPU ASIC development from concept phase to mass-production. Engage in architectural definition, silicon process and package selection, performance modeling, power budgeting, RTL development, and test implementation.
Apply LLMs, coding agents, and agentic systems - to core ASIC design problems: RTL generation, Design and Formal verification, PPA prediction and optimization.
Work as a team to build reliable, scalable and high performance software that are easy to use by hundreds of engineers worldwide. Working on backend and frontend design and development of web applications.
Build and scale data generation pipelines using LLM-based methods combined with automated quality evaluation. resulting in datasets to improve both initial training and fine-tuning of LLMs such as Nemotron.
Build and deepen technical expertise in computer-aided engineering (CAE), serving as a technical advocate and trusted resource for developers in industrial engineering, with cross-functional partners to drive adoption of NVIDIA technologies.
The role drives three interconnected systems, all aimed at accelerating NVIDIA's LLM inference stack.
Act as the lead engineer for developing the agent frameworks natively on Windows environments. You will build the technical roadmap to bring always-on, self-evolving AI assistants to GeForce RTX PCs and laptops.
Modeling and analysis of SoC & Systems algorithms and features, across datacenter, automotive, and client products. Build and deliver platforms for SOC's that enable left shift for the SW teams aligned with project milestones.
As a member of the GPU verification team, you will be responsible for verifying the ASIC design, architecture and micro-architecture of NVIDIA GPUs and SoCs using sophisticated verification methodologies.
Lead the end-to-end post-silicon execution plan, from first-silicon power-on through production qualification and volume ramp.
Analyze end-to-end performance of large-scale AI workloads across compute, network, storage, and software stacks. Design and execute rigorous performance studies to establish baselines, diagnose regressions, and quantify bottlenecks.
US, USA