NVIDIA 2026 Internships: Software Engineering - China at NVIDIA

Shanghai, China

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Not SpecifiedCompensation

InternshipExperience Level

InternshipJob Type

UnknownVisa

Technology, AIIndustries

Requirements

Actively enrolled in a university pursuing a Bachelor's, Master's, or PhD degree in Electrical Engineering, Computer Engineering, or a related field, for the entire duration of the internship
Depending on the internship role, prior course or internship experience in areas such as: Relational Databases, Linear Algebra & Numerical Methods, Operating Systems (memory/resource management), Scheduling and Process Control, Hardware Virtualization, Distributed Systems, Data Structures & Algorithms, Virtualization, Automation/Scripting, Container & Cluster Management, Debugging, Unix/Shell Scripting, Linux, Deep Learning, GPU Computing, Accelerated Computing, Validation Frameworks for Deep Learning, Deep Learning Frameworks and Libraries (NumPy, SciPy, cuBLAS, cuDNN), Data Preprocessing, Training Acceleration (CUDA, cuDNN, NCCL), Convolution Operations (cuDNN), Real-Time Inference (TensorRT)
Depending on the internship role, knowledge of programming skills and technologies such as: Java, JavaScript (including Node, React, Vue), SQL, C, C++, CUDA, OOP, Go, Python, Git, Perforce, Kubernetes and Microservices, Schedulers (LSF, SLURM), Containers (Docker), Configuration Automation (Ansible)

Responsibilities

Development Tools: Debugging complex system-level issues using Jenkins
Cloud: Supporting overall architecture and design of cloud storage infrastructure; Implementing and troubleshooting storage and data platform tools; Automating storage infrastructure end-to-end
Tools Infrastructure: Building industry leading technology by proving workflows and infrastructure; Enabling success for content running on the chip from application tracing and analysis to modeling, diagnostics, performance tuning, and debugging
Machine Learning Operations: Working with Deep Learning, GPU Computing, Accelerated Computing; Validation Frameworks for Deep Learning, Deep Learning Frameworks and Libraries (NumPy, SciPy, cuBLAS, cuDNN); Data Preprocessing, Training Acceleration (CUDA, cuDNN, NCCL), Convolution Operations (cuDNN), Real-Time Inference (TensorRT)
Building Infrastructure for Back-End Analytics

Skills

Key technologies and capabilities for this role

JenkinsRelational DatabasesLinear AlgebraNumerical MethodsOperating SystemsHardware VirtualizationDistributed SystemsData StructuresAlgorithmsVirtualizationAutomationScriptingContainer ManagementCluster ManagementDebugging

Questions & Answers

Common questions about this position

What degree program must I be enrolled in to qualify for this internship?

You must be actively enrolled in a university pursuing a Bachelor's, Master's, or PhD degree in Electrical Engineering, Computer Engineering, or a related field, for the entire duration of the internship.

What kind of experience is required for the Tools Infrastructure internship?

Course or internship experience related to Unix/Shell Scripting, Linux, Java, JavaScript (including Node, React, Vue), C++, CUDA, OOP, Go, Python, Git, GitLab, Perforce, Kubernetes and Microservices, Schedulers (LSF, SLURM), Containers (Docker), Configuration Automation (Ansible) could be required.

Is this a full-time or part-time internship?

The employment type is full time.

What qualities is NVIDIA looking for in internship candidates?

NVIDIA is seeking strategic, ambitious, hard-working, and creative individuals who are passionate about helping tackle challenges no one else can solve.

What is the compensation for this internship?

This information is not specified in the job description.

NVIDIA

Designs GPUs and AI computing solutions

About NVIDIA

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through the sale of hardware, software solutions, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. What sets NVIDIA apart from competitors is its strong focus on research and development, allowing it to maintain a leadership position in a competitive market. The company's goal is to drive innovation and provide advanced solutions that meet the needs of a diverse clientele, including gamers, researchers, and enterprises.

Santa Clara, CaliforniaHeadquarters

1993Year Founded

$19.5MTotal Funding

IPOCompany Stage

Automotive & Transportation, Enterprise Software, AI & Machine Learning, GamingIndustries

10,001+Employees

Benefits

Company Equity

401(k) Company Match

Risks

Increased competition from AI startups like xAI could challenge NVIDIA's market position.

Serve Robotics' expansion may divert resources from NVIDIA's core GPU and AI businesses.

Integration of VinBrain may pose challenges and distract from NVIDIA's primary operations.

Differentiation

NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.

The company excels in diverse markets, including gaming, data centers, and autonomous vehicles.

NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

Upsides

Acquisition of VinBrain enhances NVIDIA's AI capabilities in the healthcare sector.

Investment in Nebius Group boosts NVIDIA's AI infrastructure and cloud platform offerings.

Serve Robotics' expansion, backed by NVIDIA, highlights growth in autonomous delivery services.

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