NVIDIA 2026 Internships: Software Engineering at NVIDIA

Santa Clara, California, United States

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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, prior experience or knowledge in 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

Work on projects that have a measurable impact on NVIDIA's business during the 12-week full-time internship (varies by role)
Development Tools: Debug complex system-level issues using Jenkins
Cloud: Support overall architecture and design of cloud storage infrastructure; implement and troubleshoot storage and data platform tools; automate storage infrastructure end-to-end
Tools Infrastructure: Build industry-leading technology by providing workflows and infrastructure; enable success for content running on the chip from application tracing and analysis to modeling, diagnostics, performance tuning, and debugging
Machine Learning Operations: Work with Deep Learning, GPU Computing, Accelerated Computing; develop Validation Frameworks for Deep Learning; use Deep Learning Frameworks and Libraries (NumPy, SciPy, cuBLAS, cuDNN); perform Data Preprocessing, Training Acceleration (CUDA, cuDNN, NCCL), Convolution Operations (cuDNN), Real-Time Inference (TensorRT)
Building Infrastructure for Back-End Analytics

Skills

Jenkins

Relational Databases

Linear Algebra

Numerical Methods

Operating Systems

Hardware Virtualization

Distributed Systems

Data Structures

Algorithms

Virtualization

Automation

Scripting

Containers

Cluster Management

Debugging

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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