2026 Software Engineering Intern

About NVIDIA Internships

By submitting your resume, you're expressing interest in one of our 2026 Software Engineering Internships. We’ll review resumes on an ongoing basis, and a recruiter may reach out if your experience fits one of our many internship opportunities.

NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and digital twins is transforming the world's largest industries and profoundly impacting society — from gaming to robotics, self-driving cars to life-saving healthcare, climate change to virtual worlds where we can all connect and create.

Our internships offer an excellent opportunity to expand your career and get hands-on experience with one of our industry-leading Software teams. We’re seeking strategic, ambitious, hard-working, and creative individuals who are passionate about helping us tackle challenges no one else can solve.

Throughout the 12-week minimum full-time internship, students will work on projects that have a measurable impact on our business. We’re looking for students pursuing a Bachelor's, Master's, or PhD degree within a relevant or related field.

Potential Internship Areas

Opportunities for a 2026 Software Engineering Intern may fall into the following specialized areas, each with specific skill requirements:

Development Tools

• Debugging complex system-level issues using Jenkins.
• Course or internship experience in: Relational Databases, Linear Algebra & Numerical Methods, Operating Systems (memory/resource management), Scheduling and Process Control, Hardware Virtualization.

Cloud

• Supporting overall architecture and design of our cloud storage infrastructure.
• Implementing and troubleshooting storage and data platform tools, automating storage infrastructure end-to-end.
• Course or internship experience in: Distributed Systems, Data Structures & Algorithms, Virtualization, Automation/Scripting, Container & Cluster Management, Debugging.

Tools Infrastructure

• Building industry-leading technology by proving workflows and infrastructure, alongside a team of experts in production software development and chip design methodologies.
• Enabling success for content running on the chip from application tracing and analysis to modeling, diagnostics, performance tuning, and debugging.
• Course or internship experience in: 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).

Machine Learning Operations

• Focus on 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.

What We Seek

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.

Depending on the specific internship role, prior experience or knowledge requirements could include the following programming skills and technologies:

• 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).

Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The hourly rate for our interns is 20 USD - 71 USD. You will also be eligible for Intern benefits.

NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering a diverse work environment and is proud to be an equal opportunity employer.

Key Skills/Competency

• Python
• C++
• Java
• JavaScript
• Linux
• Distributed Systems
• Machine Learning
• Cloud Computing
• Data Structures
• Algorithms