NVIDIA 2026 Internships: Deep Learning at NVIDIA

Santa Clara, California, United States

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Not SpecifiedCompensation

InternshipExperience Level

InternshipJob Type

UnknownVisa

Artificial Intelligence, TechnologyIndustries

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
Strategic, ambitious, hard-working, and creative mindset
Passion for tackling challenging problems in AI and accelerated computing
Depending on the role, prior course or internship experience in: Deep Neural Networks, Linear Algebra, Numerical Methods, Computer Vision, Software Design, Computer Memory (Disk, Memory, Caches), CPU and GPU Architectures, Networking, Numeric Libraries, Embedded System Design and Development, Drivers, Real-Time Software, Computer Architecture (CPUs, GPUs, FPGAs or other accelerators), GPU Programming Models, Performance-Oriented Parallel Programming, Optimizing for High-Performance Computing (HPC), Algorithms
Depending on the role, programming skills and technologies: C, C++, CUDA, Python, x86, ARM CPU, GPU, Linux, Direct3D, Vulkan, OpenGL, OpenCL, Spark, Perl, Bash/Shell Scripting, Container Tools (Docker/Containers, Kubernetes), Infrastructure Platforms (AWS, Azure, GCP), Data Technologies (Kafka, ELK, Cassandra, Apache Spark), React, Go

Responsibilities

Work on 12-week full-time projects with measurable impact on NVIDIA's business in Deep Learning teams
For Deep Learning Applications & Algorithms: Develop algorithms for deep learning, data analytics, or scientific computing to improve performance of GPU implementations
For Deep Learning Frameworks & Libraries: Build underlying frameworks and libraries to accelerate Deep Learning on GPUs; contribute to software packages such as JAX, PyTorch, and TensorFlow; integrate latest library (e.g., cuDNN) or CUDA features; perform performance tuning and analysis
Optimize core deep learning algorithms and libraries (e.g., CuDNN, CuBLAS)
Maintain build, test, and distribution infrastructure for deep learning libraries and frameworks on NVIDIA supported platforms

Skills

Deep Learning

PyTorch

TensorFlow

JAX

CUDA

cuDNN

Computer Vision

GPU Architectures

Linear Algebra

Numerical Methods

Software Design

Embedded Systems

Real-Time Software

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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