CUDA Math Libraries intern

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow, Deep Learning, Natural Language Processing (NLP)

Job Description

NVIDIA is looking for software engineering interns for its GPU-accelerated high-performance computing (HPC) mathematical libraries team. Did you know these GPU accelerated mathematical libraries developed by NVIDIA are being used in a wide range of applications in scientific computing, engineering, and deep learning? Around the world, leading commercial and academic organizations are revolutionizing AI, scientific and engineering simulations, and data analytics, using data centers powered by GPUs. Applications include: healthcare, VR, deep learning, autonomous vehicles and countless others. If the idea of contributing to such state of the art research and development excites you, come join our dynamic team and help us build GPU accelerated software libraries which will be used by the entire world.

During your internship, you will working with senior software engineers in the libraries team who will provide mentorship and guide you in developing highly optimized algorithms and numerical methods. Projects will involve implementing new numerical algorithms, defining APIs, analyzing performance, finding appropriate solutions for difficult numerical corner cases, and other general software engineering work.

What you’ll be doing:

• Prototype and develop numerical algorithms for high-performance math libraries.
• Analyze the performance of GPU or CPU implementations and find opportunities for improvements and implement them.
• Collaborate with team members and other stakeholders to understand software use cases and requirements.

What we need to see:

• Studying towards a degree in Computer Science, Applied Math, Engineering, or related field.
• Excellent C/C++ programming, debugging, performance analysis, and test design skills.
• Strong algorithms and numerical methods fundamentals.
• Ability to work independently and manage your own development effort.
• Good teamwork, communication, and documentation habits.

Ways to stand out from the crowd:

• Parallel programming experience using multi-threading or MPI.
• Experience developing and optimizing numerical methods HPC software.
• Knowledge of CPU or GPU hardware architecture.
• GPU programming experience (CUDA or OpenCL) is desired but not required.
• Experience with low level programming in C or assembly.
• Exposure to floating-point arithmetic and numerical error analysis.
• A scripting language, preferably Python.