Deep Learning Intern

What you'll do

• Develop algorithms for deep learning, data analytics, or scientific computing to improve GPU performance.
• Build underlying frameworks and libraries to accelerate deep learning on GPUs.
• Contribute directly to software packages such as JAX, PyTorch, and TensorFlow.
• Integrate the latest library features like cuDNN or CUDA into existing software.
• Optimize core deep learning algorithms and libraries for high-performance computing.
• Maintain build, test, and distribution infrastructure for deep learning frameworks on NVIDIA platforms.
• Perform performance tuning and analysis on deep learning software and hardware implementations.

What we're looking for

• Must be actively enrolled in a university pursuing a B.S., M.S., or Ph.D. degree in Electrical Engineering, Computer Engineering, or a related field.
• Must clearly indicate an anticipated graduation date (month and year) on the resume or CV.
• Course or internship experience in Deep Neural Networks, Linear Algebra, Numerical Methods, and/or Computer Vision may be required.
• Course or internship experience in Software Design, Computer Memory, CPU/GPU Architectures, Networking, and Numeric Libraries may be required.
• Course or internship experience in Embedded System Design, Drivers, and Real-Time Software may be required.
• Course or internship experience in Computer Architecture, GPU Programming Models, Parallel Programming, and HPC optimization may be required.
• Prior experience or knowledge in C, C++, CUDA, Python, x86, ARM CPU, GPU, Linux, Direct3D, Vulkan, OpenGL, OpenCL, Spark, Perl, Bash/Shell Scripting, Docker, Kubernetes, AWS, Azure, GCP, Kafka, ELK, Cassandra, Apache Spark, React, or Go may be required.