NVIDIA 2027 Internships: Deep Learning
About The Position
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 Deep Learning 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 full-time internship, students will work on projects that have a measurable impact on our business. We’re looking for students pursuing a B.S., M.S., or Ph.D. degree within a relevant or related field. Potential Internships in this field include: Deep Learning Applications and Algorithms Developing algorithms for deep learning, data analytics, or scientific computing to improving performance of GPU implementations Deep Learning Frameworks and Libraries Building underlying frameworks and libraries to accelerate Deep Learning on GPUs Contributing directly to software packages such as JAX, PyTorch, and TensorFlow, integrating the latest library (e.g., cuDNN) or CUDA features, performance tuning, and analysis Optimizing core deep learning algorithms and libraries (e.g., CuDNN, CuBLAS), maintaining build, test, and distribution infrastructure for these libraries and deep learning frameworks on NVIDIA supported platforms
Requirements
- Actively enrolled in a university pursuing a B.S., M.S., or Ph.D. degree in Electrical Engineering, Computer Engineering, or a related field, for the full duration of the internship
- Anticipated graduation date (month and year) must be clearly indicated on a resume or CV
- Course or internship experience related to Deep Neural Networks, Linear Algebra, Numerical Methods and/or 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
- Course or internship experience related to Computer Architecture (CPUs, GPUs, FPGAs or other accelerators), GPU Programming Models, Performance-Oriented Parallel Programming, Optimizing for High-Performance Computing (HPC), Algorithms, Numerical Methods
- 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
- Developing algorithms for deep learning, data analytics, or scientific computing to improving performance of GPU implementations
- Building underlying frameworks and libraries to accelerate Deep Learning on GPUs
- Contributing directly to software packages such as JAX, PyTorch, and TensorFlow, integrating the latest library (e.g., cuDNN) or CUDA features, performance tuning, and analysis
- Optimizing core deep learning algorithms and libraries (e.g., CuDNN, CuBLAS), maintaining build, test, and distribution infrastructure for these libraries and deep learning frameworks on NVIDIA supported platforms
Benefits
- Intern benefits
- Internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience.
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What This Job Offers
Job Type
Full-time
Career Level
Intern
Education Level
Ph.D. or professional degree
