NVIDIA 2027 Internships: Deep Learning
About The Position
NVIDIA is hiring interns for its Deep Learning teams. During a 12-week, full-time internship, students will work on projects with measurable business impact. Potential areas include developing deep learning, data analytics, and scientific computing algorithms; improving GPU implementation performance; and building frameworks and libraries that accelerate deep learning on GPUs. Work may include contributing to JAX, PyTorch, and TensorFlow; integrating libraries such as cuDNN or CUDA features; performance tuning and analysis; optimizing deep learning algorithms and libraries; and maintaining build, test, and distribution infrastructure for NVIDIA-supported platforms. The role is intended for students pursuing a relevant bachelor's, master's, or Ph.D. degree who will remain actively enrolled for the full internship. Applications are reviewed on an ongoing basis.
Requirements
- Must be actively enrolled in a university pursuing a B.S., M.S., or Ph.D. 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 the resume or CV.
- Depending on the internship role, relevant prior experience or knowledge may include C, C++, CUDA, Python, x86, ARM CPU, GPU, Linux, Direct3D, Vulkan, OpenGL, OpenCL, Spark, Perl, Bash/Shell Scripting, Docker or other container tools, Kubernetes, AWS, Azure, GCP, Kafka, ELK, Cassandra, Apache Spark, React, or Go.
- Depending on the internship role, relevant coursework or internship experience may include deep neural networks, linear algebra, numerical methods, computer vision, software design, computer memory, CPU and GPU architectures, networking, numeric libraries, embedded system design and development, drivers, or real-time software.
- Depending on the internship role, relevant coursework or internship experience may include computer architecture, GPU programming models, performance-oriented parallel programming, high-performance computing optimization, algorithms, or numerical methods.
Responsibilities
- Develop algorithms for deep learning, data analytics, or scientific computing and improve the performance of GPU implementations.
- Build frameworks and libraries to accelerate deep learning on GPUs.
- Contribute to software packages such as JAX, PyTorch, and TensorFlow, including integration of library or CUDA features, performance tuning, and analysis.
- Optimize core deep learning algorithms and libraries such as cuDNN and cuBLAS.
- Maintain build, test, and distribution infrastructure for deep learning libraries and frameworks on NVIDIA-supported platforms.
Benefits
- Intern benefits are offered; specific benefits are not listed.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Intern
