• Designing and developing software for testing and analysis of our codebases • Building scalable automation for build, test, integration, and release processes for publicly distributed deep learning libraries • Developing throughout the software stack, from the user experience down to the cluster and database layers • Configuring, maintaining, and building upon deployments of industry-standard tools (e.g. Kubernetes, Jenkins, Docker, CMake, Github, Gitlab, Jira, etc) • Advancing state of the art in those industry-standard tools

• Analyze brand-new DL networks (LLM etc.), identify and prototype performance opportunities to influence SW and Architecture team for NVIDIA's current and next-gen inference products. • Develop prototypes of the fastest kernels on present and future NVIDIA GPUs. • Define hardware and software setups along with measurements to evaluate performance, power consumption, and accuracy in current and upcoming chips. • Collaborate across the company to guide the direction of next-gen deep learning HW/SW by working with architecture, software, and product teams.

• Capture business requirements, translate requirements into functional design, user stories, technical design, drive end to end integration testing, support data set up and issue remediation during UAT, manage development team activities, develop hypercare support model • Define and architect AI agents for Supply Chain use cases, using the right frameworks, multi-agent coordination, RAG, deployment, monitoring, and life cycle management. • Be hands on in quick proof of concepts development to demonstrate technical feasibility and implement enterprise grade Agentic Supply Chain solutions  • Partner with Enterprise IT engineering, product, and research teams while evaluating LLMs, agentic frameworks, and NVIDIA’s own NeMo technologies. • Ensure integration with enterprise IT and Operations data sources and Industry’s best Agentic platforms with strong content security focus. • Drive architectural decisions across deployment models (on-prem, cloud, hybrid, containerized) to deliver scalable, reliable, and efficient solutions. • Lead design reviews, develop technical documentation, and guide developers in principles of architecture and code development. • Champion observability, monitoring, versioning, and telemetry to ensure trustworthy and auditable AI agents. • Influence Supply Chain Operations adoption of the platform by partnering with stakeholders across IT, supply chain and serve as a reference adopter providing feedback to strengthen NVIDIA’s ecosystem.

• As a key MMPLEX Video Design team member, you will document, implement, and deliver fully verified, high-performance, low-area, and power-efficient designs to achieve the design targets and specifications. • Participate in video-related design and analyze architectural trade-offs based on features, performance requirements, and system limitations. • Craft micro-architecture, implement in HLS/RTL, and deliver a fully verified, synthesis/timing clean design. • Collaborate and coordinate with architects, other designers, pre- and post-silicon verification, SOCD, emulation, back-end, and bringup teams to accomplish your tasks.

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. This is our life’s work — to amplify human imagination and intelligence. AI becomes more and more important in self-driving car. NVIDIA is at the forefront of the AI-City and self-driving revolution and providing powerful solutions for them. All these solutions are based on GPU-accelerated libraries, such as CUDA, cuDNN and TensorRT, etc.Now, we are now looking for CPU computing interns based in Shanghai. Join the team to provide the powerful AI solution to the entire world! What you’ll be doing: • Analyze the GPU computing issues and write some test code for them. • Write some documentation about the analysis of the issue.

We’re working on the next generation of recommendation tools and pushing the boundaries of accelerating model  training and inference on GPU. You’ll join a team of ML, HPC and Software Engineers and Applied Researcher developing a framework designed to make the productization of GPU-based recommender systems as simple and fast as possible.  What you’ll be doing: In your role as CUDA Engineer Intern you will be profiling and investigating the performance of optimized code together within our HPC team. Part of this job will be to perform tests, unit tests and validate the numerical performance and correctness of the code. You will discuss your approach and results together with our CUDA engineers.

GPU System Architect team’s work scope covers whole GPU pipeline(graphics, compute pipeline, memory system) and multi GPU, CPU and CPU interconnection, which provides good opportunity to deeply learn the latest cross unit new features in the new GPU architectures. The team works as the safety net of the chip. We catch function bugs in the HW by randomly generating tests and running them in various pre-silicon full chip platforms and debugging the failures. This works provides a good full chip view of GPU and has a big space to innovate. What you’ll be doing: • Get familiar with various GPU workload’s composition • Learn about what’s the usual feature metrics for GPU workload • Design and implement inventive solution to efficiently extract features from GPU workload • Verify the solution using direct and random GPU workload • Design and implement inventive solution simplify GPU workload while keeping the required features • Design and implement inventive solution to generate GPU workload according to required features • Design and implement inventive solution to generate GPU workload which has the same feature with a given test and randomize other (required) features • Thoroughly verify the solution on GPU functional simulator/full chip RTL/emulation/silicon platform. • Provide detailed and organized documentation and report out for the project.

• Craft and develop robust inferencing software that can be scaled to multiple platforms for functionality and performance • Performance analysis, optimization and tuning • Closely follow academic developments in the field of artificial intelligence and feature update TensorRT • Provide feedback into the architecture and hardware design and development • Collaborate across the company to guide the direction of machine learning inferencing, working with software, research and product teams • Publish key results in scientific conferences