CUDA Libraries and Frameworks Product Marketing Manager

We are seeking a mission-driven Developer Relations Manager focused on Foundational AI Research to engage leading academic labs advancing the next generation of AI models, systems, and methods. In this role, you will work directly with top researchers building frontier AI systems, including large language models, multimodal models, reasoning systems, training methods, inference systems, model serving, and scalable AI infrastructure. You will help researchers adopt NVIDIA’s AI and accelerated computing platforms to push the boundaries of model performance, efficiency, and scale. The ideal candidate brings deep technical credibility in foundational AI, strong research engagement experience, and hands-on expertise in either AI inference research or AI training research. What you'll be doing: Serve as a trusted technical advisor to leading academic AI labs working on foundation models, LLMs, multimodal AI, reasoning, training, inference, and AI systems. Identify high-impact research workloads where NVIDIA software, systems, and accelerated computing platforms can advance model performance, scale, and efficiency. Engage principal investigators, postdocs, graduate researchers, and lab leadership to understand research goals, technical blockers, infrastructure needs, and collaboration opportunities. Track frontier AI research across papers, benchmarks, open-source projects, and academic labs to identify emerging trends and future platform opportunities. Partner with Research Account Managers, Solution Architects, Product, Engineering, and Business Development teams to support researcher adoption and long-term engagement. Represent researcher needs internally by translating academic feedback into actionable insights for product roadmaps, developer programs, education, and platform strategy. Support NVIDIA participation in major AI, ML, and systems research venues through technical content,

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE ROLE Are you passionate about advancing the application of artificial intelligence? We are looking for a Software Engineer focused on ML performance to join our dynamic team. This role is ideal for someone who thrives in a fast-paced startup environment and is eager to make significant contributions to the exciting field of LLM Inference. If you are a backend engineer who thrives on making things faster and is excited about open-source ML models, we look forward to your application. EXAMPLE INITIATIVES You'll get to work on these types of projects as part of our Model Performance team: Baseten Embeddings Inference: The fastest embeddings solution available The Baseten Inference Stack Driving model performance optimization RESPONSIBILITIES Implement, refine, and productionize cutting-edge techniques (quantization, speculative decoding, kv cache reuse, chunked prefill and LoRA) for ML model inference and infrastructure. Deep dive into underlying codebases of TensorRT, PyTorch, TensorRT-LLM, vllm, sglang, CUDA, and other libraries to debug ML performance issues. Apply and scale optimization techniques across a wide range of ML models, particularly large language models. Collaborate with a diverse team to design and implement innovative solutions. Own projects from idea to production. REQUIREMENTS Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or related field. Experience with one

We are hiring senior engineers to work on the CUDA driver, a core component of our platform for accelerating general purpose computation on the GPU. Our team delivers features and improvements to better realize the potential of NVIDIA hardware for a growing range of computational workloads, ranging from deep learning, scientific computation, and self-driving cars to video games and virtual reality! CUDA defines a unified programming model across a range of system configurations and hardware capabilities. To accomplish this, the CUDA driver interacts with GPU hardware, kernel mode drivers, switches and the operating system. What you'll be doing: As a member of our team, you will use your design abilities, coding expertise, and creativity to deliver the best Compute platform in the world. You will craft elegant solutions to exciting problems and craft the future direction of CUDA as you collaborate with your peers across NVIDIA. You will evangelize, architect, and implement new CUDA features You'll oversee and drive development efforts across multiple teams Collaborate with members of hardware architecture teams Help define forward-looking improvements to the CUDA APIs and programming model Design and maintain performance and precision modeling Write effective, maintainable, and well-tested code Develop code for multiple operating systems What we need to see: Bachelor of Science or Master of Science degree in Computer Science, Electrical Engineering, or related field (or equivalent experience) 15&#43; years of relevant systems software development experience Strong C programming skills </

Job Title Senior Software Engineer - Image Reconstruction (C++/CUDA) Job Description Build the GPU-native engine that turns raw CT physics into life-saving images inside Philips scanners worldwide , wringing maximum performance from constrained hardware at the frontier of C&#43;&#43;, CUDA, and AI alongside world-class physicists. Your role: Help r e-architect an entire CT image-reconstruction pipeline, from raw detector physics to the final clinical image, as a fully GPU-native, massively scalable platform in C&#43;&#43; and CUDA. Work shoulder-to-shoulder with physicists, algorithm architects, and platform engineers, translating complex signal and image-processing models into high-performance GPU implementations that balance image quality against compute cost. Collaborate with CT platform teams around the globe to design, build, test, and deploy an industry-leading reconstruction software platform. Push the frontier of performance engineering and applied AI by profiling, parallelizing, optimizing memory use across caches and shared memory, and pioneering learned models that replace expensive physics with fast, accurate equivalents. This hybrid role is based in Cleveland, OH, with three flexible days in the office and two remote days each week . Regular travel is not typically </sp

About the Team The Workload Networking team is responsible for the collective communication stack used in our largest training jobs. Using a combination of C++ and CUDA we work on novel collective communication techniques that enable efficient training of our flagship models on our largest custom built supercomputers. The models we train are key ingredients to the AI research progress at OpenAI and the field as a whole, and we continually incorporate learnings from our entire research org into our training platform. About the Role As a Software Engineer, Networking you will design and implement custom networking collectives that are tightly integrated into our training stack. We’re looking for people who have a background in low level performance critical software. Experience with collective communication is a bonus. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Collaborate closely with ML researchers to design and implement efficient collective operations in C++ and CUDA. Ensure that our largest training jobs take full advantage of the different network transports used in our supercomputers. Work on simulations to inform our future supercomputer network designs. You might thrive in this role if you: Have written distributed algorithms using RDMA in the past. Are comfortable writing low level performance sensitive CPU and/or GPU code. Are familiar with network simulation techniques. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voic

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