Nvidia Manager Jobs

Our work at NVIDIA is dedicated towards a computing model focused on visual and AI computing. For two decades, NVIDIA has pioneered visual computing, the art and science of computer graphics, with our invention of the GPU. The GPU has also shown to be spectacularly effective at solving some of the most complex problems in computer science. Today, NVIDIA’s GPU simulates human intelligence, running deep learning algorithms and acting as the brain of computers, robots and self-driving cars that can perceive and understand the world. We are looking to grow our company and teams with the smartest people in the world and there has never been a more exciting time to join our team! The AI Infrastructure Product Design team creates software used by engineers and researchers to prepare data, run complex workflows, and understand results. This internship offers ownership of a defined product problem from early research through a tested design and implementation handoff. Designers on this team often move between Figma and working HTML prototypes, and may hand off HTML directly to engineering. This work calls for a high standard of visual and interaction design alongside technical fluency. The role is a good fit for someone who enjoys making technically complex systems easier to understand and who uses large language models and software agents thoughtfully as part of their design and prototyping process. What you will be doing: Own a focused design project for an internal AI infrastructure product, from understanding the problem through a validated design and implementation handoff. Interview engineers and researchers, map their workflows, and turn the findings into clear product requirements, user flows, and interaction models. Create precise, implementation-ready interface designs and interactive prototypes in Figma and HTML/CSS, with careful attention to typography, hierarchy, spacing, visual consistency, interactio

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. Our team builds AI-driven software systems for Circuit Design: combining automation algorithms, DL models and agentic workflows to accelerate end-to-end design automation. Come join this integral team in our Circuit Solutions Group! What you'll be doing: Work within a multi-functional team on various projects involving Pre-silicon and Post Silicon custom circuit design and related data, Circuit/Layout Optimization and Spice correlation Research and implement techniques on frontier solutions of electronic design automation. Build and innovate agentic AI solution for VLSI design problem. Responsible for analyzing the problem or datasets, raise and validate hypotheses, design and build models and algorithm until they reach the desired QOR. What we need to see: MS (or equivalent experience) with 3+ years’ experience or PhD with 1+ years’ experience in Electrical/Computer Engineering degree Experience in the following fields is a strict requirement for this role: Combinatorial Optimization, Agentic AI and large language models, Machine Learning for Chip Design & EDA Experience in Algorithms/Data Structures/

We're seeking a highly technical and hands-on Technical Marketing Engineer to help drive NVIDIA's Robotics and Physical AI initiatives in China. You will work closely with engineering, product, sales, and marketing teams, as well as leading robotics developers, OEMs, and ecosystem partners to evaluate our technologies, improve developer experience, and support adoption of NVIDIA robotics solutions. China is one of the world's most advanced and fast-moving robotics ecosystems. You will work closely with internal and external stakeholders to understand real-world requirements, emerging use cases, developer needs, and help translate these insights into product feedback, technical guidance, and improved solutions. You should be comfortable working in a multifaceted environment and possess excellent written and verbal communication skills. The ability to work independently and the motivation to learn new technologies and skills are essential. What you'll be doing: Own and lead the development of high-impact technical marketing content that clearly articulates the value and differentiation of NVIDIA robotics platforms. Evaluate developer experience, performance, and end-to-end robotics workflows through hands-on testing, benchmarking, debugging, and validation, and provide actionable feedback to engineering and product teams. Work with internal teams and robotics ecosystem partners to understand technical requirements, use cases, and development challenges to help improve NVIDIA robotics solutions. Develop and validate technical demos, reference implementations, benchmarks, and workflows on Physical AI platforms. Collaborate across engineering, research, product, sales, marketing, and solution architecture teams driving strategic alignment and ensuring b

Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brain of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As a NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. NVIDIA is looking for an AI Computer Engineer to join its NVIDIA Infrastructure Specialists (NVIS) team. Academic and commercial groups around the world are using NVIDIA products to revolutionize deep learning and data analytics, and to power data centers. Join the team building many of the largest and fastest AI factory systems in the world! NVIDIA is looking for someone with the ability to work on a dynamic customer-focused team that requires excellent interpersonal skills. This role will be interacting with customers, partners and internal teams, to analyse, define and implement large scale AI Factory projects. The scope of these efforts includes a combination of Networking, System Design and Automation while being the face to the customer. What you will be doing: Primary responsibilities will include deploying, managing, and maintaining AI infrastructure for new and existing customers. Be the domain expert with customers during planning calls through implementation. Handover-related documentation and perform knowledge transfers required to support customers as they begin rolling out some of the most sophisticated systems in the world! Provide feedback to internal teams such as opening bugs, documenting workarounds, and suggesting improvements. What we need to see: Bachelor’s degree in computer science, Electrical Engineering, or a related field, or equivalent experience.

NVIDIA DGX Cloud is an AI Factory designed to power the next generation of AI and industrial-scale breakthroughs. As a Principal Engineer for Security Architecture, within our Security Engineering organization, you will own a core security domain of the AI factory: the architecture, the paved road that delivers it, and much of the code underneath. You will hold the security design bar across DGX Cloud from inside the teams doing the building, and this is a founding seat on a new team. Security Engineering is a new organization at DGX Cloud, accountable for the security outcome of the platform, and Security Architecture is the function inside it that holds the design bar. Security here is fleet horizontal and stack vertical, so your work will cross every DGX Cloud engineering organization: you will embed with the teams building GPU clusters, control planes, and services, join their designs as a participant rather than an approver, and leave behind systems in which an entire class of risk is no longer possible. There is no architecture review board here and no approval queue. You are a senior IC with deep security domain knowledge, and the security bar holds because you helped set it and then helped ship it. What You Will Be Doing: Own a Security Domain End to End: Take architectural ownership of a core domain of DGX Cloud security, from the design through the system running in production. That could be tenant and GPU workload isolation, workload identity, infrastructure and network, supply-chain provenance, hardened baselines and patching, or deploy-time policy and admission control. Embed with the Teams Building It: Join the design early, write the code, and help land it. The posture is not "you did this wrong." It is "here are the considerations we need to meet, I will help, let's go to work." Build Paved Roads, Not

Own the end-to-end execution that takes NVIDIA DRIVE Autonomous Driving Software (NDAS) from product definition to production deployment across major OEM vehicle lines. We are seeking an accomplished engineering execution leader to lead adaptation, integration, validation, and production readiness of NVIDIA Autonomous driving solution (NDAS) for strategic automotive OEMs. You will work between NVIDIA product and engineering teams and the OEM's vehicle, software, systems, and validation groups. The role involves converting an agreed feature set into a deliverable vehicle program. This entails clarifying requirements, coordinating interfaces and responsibilities, setting the engineering plan, building consistent productization workflows, and managing issue resolution. You will influence teams such as systems engineering, autonomous-driving development, platform software, functional safety, cybersecurity, quality, validation, release, and customer engineering. What You'll Be Doing: Lead NDAS execution with major OEMs across the full program lifecycle — from feature definition and technical alignment through vehicle integration, validation, launch, and production ramp — owning the coordinated engineering plan per vehicle line, including requirements, architecture, adaptation scope, achievements, staffing, validation strategy, release criteria, and production-readiness gates. Translate OEM vehicle requirements and use cases into clear commitments for NVIDIA teams, while ensuring OEM partners understand product capabilities, constraints, assumptions, and the work required on their side; build scalable, repeatable workflows for adapting NDAS to specific OEM platforms, vehicle architectures, sensor configurations, compute platforms, networks, and development processes. Drive multi-functional implementation across autonomous-driving features, systems, platform software, vehicle integratio

The NVIDIA PerfTech team is looking for a talented C++ Software Engineer to help build the next generation of AI-powered developer tools. You will apply strong C++ and software-engineering fundamentals while gaining hands-on experience with agentic workflows, retrieval systems, and AI services. In this role, you will contribute to Genie, NVIDIA’s company-wide AI knowledge and developer-productivity service. You will work across C++ tools and AI services to help engineers find information, understand complex systems, and work more effectively. What You’ll Be Doing: Develop production-quality C++ components, APIs, and integrations for NVIDIA’s AI-powered developer-tools ecosystem. Build capabilities connecting native C++ tools with Genie’s retrieval and agentic features. Contribute to agentic workflows, retrieval systems, ingestion pipelines, MCP tools, APIs, and enterprise integrations. Build benchmarks and improve retrieval quality, reliability, performance, and resource usage. Own features from investigation and design through implementation, testing, and delivery. Collaborate with graphics, software, and hardware teams developing performance-analysis and developer tools. What We Need to See: Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience. 5+ years of modern C++ programming skills gained through professional experience, internships, or substantial technical projects. Good understanding of data structures, algorithms, object-oriented design, multithreading, debugging, and testing. Ability and motivation to work across C++ systems and Python-based AI services. Familiarity with AI-powered applications, agentic workflows, retrieval systems, or related technologies. Abil

NVIDIA Research is seeking extraordinary networking innovators to join our NVResearch team. As a research intern on this team, you will contribute to the development of future high-performance networking and computing systems. We are seeking a balanced background of research excellence in building systems and a deep understanding and broad perspective across the fields of computer architecture and communication systems for distributed computation. NVIDIA has pioneered programmable GPUs and the CUDA language, and this visionary Research team will take those technologies to the next level with its creative ideas and new inventions. This position offers you the opportunity to have a real impact while working with some of the most creative and forward-thinking people in the world who are here at this dynamic, technology-focused company. What you'll be doing: Develop algorithms and design hardware and software, extending the state of the art in computing, networking, and other technology areas surrounding NVIDIA's business. Invent new techniques, technologies, methodologies, processes, and devices, to enable new products or types of products. Deliverable results include prototypes, patents, and publications. Contribute to research that informs NVIDIA's technology direction 5-10 years out. Work focuses on long-horizon problems rather than products currently shipping or in development, except as to how they can be extended and improved. Projects can include but are not limited to: optimizing communication stacks for AI training and inference, designing network protocols and congestion control, co-designing AI systems across software and hardware, developing circuits and microarchitecture for network controllers and switches, and architecting networks built on optical switching and silicon photonics. What we need to see: Pursuing a

NVIDIA is seeking a Senior System Architect: Heterogeneous EDA Systems to solve a complex challenge in accelerated computing: Failure Attribution at Scale. As EDA or equivalent experience workloads scale across thousands of heterogeneous nodes, a single failure can cause massive resource waste. We need an engineer to develop and build an automated framework. This framework will ingest telemetry from CPU and GPU clusters to identify the root cause of job failures in real-time. It will distinguish between hardware faults, infrastructure instability, and software defects. What you'll be doing: Architect Failure Attribution Frameworks: Build a scalable "flight recorder" for EDA jobs that captures high-fidelity state across the CPU, GPU, and Fabric at the moment of failure. Build automated diagnostics that correlate GPU XID errors, PCIe bus failures, and CUDA memory exceptions. Connect these errors with system-level events such as OOM kills or NUMA-related hangs. Distributed Logging & Tracing: Implement low-overhead tracing mechanisms (using tracing tools or custom agents) that provide access to job execution across multi-node Slurm or Kubernetes clusters. Root Cause Automation: Develop heuristics and models based on machine learning to classify failures as "Hardware Fault," "Software Bug," or "Environment Issue." This reduces the Mean Time to Identify (MTTI) for R&D teams. Resiliency Engineering: Work closely with hardware and infrastructure teams to define "signals of impending failure," enabling proactive job migration or check-pointing before a crash occurs. What we need to see: Distributed Systems Mastery: BS, MS, or PhD in Computer Science or Electrical Engineering (or equivalent experience) with 6+ years in systems programming. Experience building automated

NVIDIA is transforming how the world uses AI, cloud, and accelerated computing, and trust is at the center of that mission. Our Attestation and Trust Services team builds the secure cloud services that show customers their NVIDIA platforms are healthy, resilient, and ready for their most important workloads. In this role, you help design and run services that sit at the intersection of hardware, security, and large-scale distributed systems. We partner closely with security, silicon, platform, and cloud teams to bring new ideas into reliable production services that people rely on every day. We care about building systems that last, supporting each other, and creating space for learning and experimentation. If you enjoy solving complex problems, keeping services running smoothly, and collaborating with teammates from many disciplines, we would love to talk with you! What you’ll be doing: Your main focus will be on building and managing our core attestation cloud services. Day-to-day responsibilities include crafting APIs and integrations, boosting reliability, and working alongside NVIDIA teams to convert hardware trust mechanisms and standards into production-ready solutions. You will contribute significantly to shaping how customers verify that NVIDIA platforms are secure and prepared for their workloads. Crafting and evolving attestation cloud services, APIs, and SDK/CLI integration points that confirm the integrity of NVIDIA platforms across data center, AI, networking, and partner environments. Improving reliability and operational maturity through SLOs/SLIs, alerting, runbooks, incident response, and safe rollout practices. Crafting resilient service behavior that handles dependency failures, caching challenges, regional issues, customer-side resilience needs, and graceful degradation. Architecting trust-material distribution for certificate status, re

NVIDIA is seeking a world-class computer architect to contribute to the development of future high-performance computing systems, with a focus on enhancing the power-constrained performance of the hardware. Ideal candidates will have a strong track record of understanding and analyzing memory systems architecture to improve performance per watt (perf/W) and performance per millimeter (perf/mm). A broad perspective across the field of computer architecture and depth in the area of power, performance, and area (PPA) analysis is highly desirable. NVIDIA has pioneered programmable GPUs and the CUDA language and is a world leader in high-performance computing technology, with aggressive plans for future processors. This position offers the opportunity to have a real impact in a fast-moving, technology-focused company. What you will be doing: Develop innovative high-performance processor and system architectures, focusing on the memory system and energy efficiency. Develop architecture and micro-architecture features to improve the state-of-the-art in GPU memory systems, optimizing along the axes of perf/W, perf/mm, and perf/$. Develop and enhance architecture prototype models for power and noise analysis. Participate in performance and power simulation of features to analyze, define, and improve energy per byte. Analyze benchmarks, application workloads, and performance/power simulation and emulation results to identify areas for architecture optimizations. Debug power, performance, and functional issues with high-level models, RTL simulation and emulation, silicon, and systems. Collaborate with outside partners on system infrastructure. What we want to see: 10+ yrs of experience in CPU/GPU architecture, memory systems design with a focus on energy efficiency in the system. Bachelor

NVIDIA is now looking for a Senior Memory System Engineer to join our ASIC Memory Subsystem team! As a Senior Systems Engineer at NVIDIA, you'll join a group of hardworking engineers to develop and architect innovative Memory Solution for Tegra SoCs. In this position, you'll make a real impact in a multifaceted, technology-focused company. You will work with memory controller/PHY and Platform / System architect, Firmware, SI/PI, Memory suppliers to design and architect cutting edge, high speed and lower power memory technology for NVIDIA CPUs and SOCs. What You Will Be Doing: Analyze future DDR/LPDDR/HBM technologies to determine optimum performance, power, function and RAS in memory for Next generation SOC and Systems. Collaborate with ASIC Architects, Designers, Software and Firmware SW/FW teams to drive memory technology and associated requirements for memory controllers. Define Memory module, Package, and PCB layouts appropriate to the system workloads Debug and bring up memory evaluation / validation and failure issues on memory technology. Collaborate with DRAM suppliers and industry partners on to develop memory and memory related component technology. What We need to see: Bachelor's degree or master’s degree in Electrical Engineering, Computer Engineering (CE), or a related field (or equivalent experience) 10 years of proven track record in DRAM design, module design, or memory sub system design. Deep understanding and strong fundamental of memory design, features, ECC algorithm, SI and PI (Training algorithm) in DDR, LPDDR, and HBM. Strong understanding of memory sub system level interaction with Cache, Memory controller and PHY. Experience in the design, bring-up and validation for memory failure analysis Experience with Python, C/C

NVIDIA is at the forefront of the AI and robotics revolution, and NVIDIA’s robotics teams are on a mission to build the essential technology that can enable any company to become a robotics company. The Seattle Robotics Lab is uniquely positioned at the intersection of open academic research and real-world industry impact, pursuing fundamental and applied robotics research across the full robotics stack, including perception, planning, control, reinforcement learning, imitation learning, simulation, and robotics foundation models. This research aims to transform research paradigms, transfer into NVIDIA’s robotics and simulation products, and create new robotics markets for the world. The Seattle Robotics Lab has published over 500 research papers, including many influential works that have been presented at top robotics, AI, and computer vision conferences. These works include BayesSim , cuRobo , DeXtreme , DiSECT , Factory , GraspNet , IndustReal , ITPS , LAPA , <a href="h

Join the NVIDIA's Solutions Engineering team that is reshaping the future of driving! Our goal is to build and deploy scalable solutions for autonomous vehicles and as a result, create safer and more efficient roads. Our team is hands-on, passionate about practical results, and values diversity. You will help craft the application software architecture by working closely with external partners developing on our platform and on collaborations across multiple teams within NVIDIA working on autonomous vehicles. You will also advance and refine the overall drivability of our solution, focusing on integration challenges and using your deep analytical skills to tease through the complexity of the system to find effective solutions. NVIDIA is widely considered to be one of the technology world’s most desirable employers, and is committed to fostering a diverse work environment and proud to be an equal opportunity employer. If you are passionate in bringing autonomous vehicles into the world and see the solution come together, we would like to hear from you! What you'll be doing: Shape the application architecture internally, with a focus on perception & sensor fusion, by collaborating closely with architecture and software development teams. Integrate and adapt NVIDIA solutions in target vehicles, ensuring that both perception and sensor fusion are adapted and tuned to meet the desired driving performance and functionality. Lead bring-up activities and provide technical support to resolve functional and perception & sensor fusion related issues. Perform and leverage in-vehicle and simulation test drives for functional and performance analysis on the recorded data. Work with our partners to efficiently integrate hardware and software components, understand the system architecture, profile performance, identify bottlenecks, and drive optimization <

NVIDIA DGX Cloud is an AI Factory designed to power the next generation of AI and industrial-scale breakthroughs. As the Distinguished Engineer for Security Architecture, within our Security Engineering organization, you will set the security design bar for an AI factory of hundreds of thousands of GPUs, and then build against it alongside the teams. This is the founding architecture seat in a new organization. Security Engineering is a new organization at DGX Cloud, accountable for the security outcome of the platform, and this is the architecture function inside it. You will define the security design standard for DGX Cloud, a bar that sits above the company floor, and hold it from inside the teams doing the building. Security here is fleet horizontal and stack vertical, so your scope runs from the hardware root of trust and the hardened baseline, through tenancy and GPU workload isolation, to the services and APIs built on top, across every DGX Cloud engineering organization. A small team of Principal Engineers will report to you and hold the bar at domain depth. This is still a hands-on seat, and you stay in the design with them. You will also serve as DGX Cloud's technical interface into NVIDIA's central security organization. There is no architecture review board here and no approval queue; the bar holds because the strongest security engineers in the room helped set it and helped ship it. What You Will Be Doing: Set the DGX Cloud Security Bar: Own the security design standard across DGX Cloud (tenancy, GPU workloads, identity, supply chain, and isolation) and make it concrete. Reference architectures, golden paths, and requirements engineers can actually build against, not a policy library. Hold the Bar by Building: Embed with engineering teams on real work: join the design, learn the code, help ship the thing rather than grade it afterward.