Software Engineering Intern, JAX - Fall 2026

NVIDIA•Us, CA

About The Position

Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, encouraging environment where everyone is inspired to do their best work. Come join the team and see how we can make a lasting impact on the world. Join our team in developing sophisticated performance optimizations for deep learning frameworks using JAX, a powerful tool that combines automatic differentiation with XLA for high-performance machine learning research. We are committed to creating a fast, modular, and coordinated platform for a wide range of deep learning solutions. Together, we will push the boundaries of AI and scientific computing to new frontiers! If you're a skilled programmer with a knack for elegant design and a passion for developing customer-facing solutions, this is your opportunity to make a lasting impact on our groundbreaking work. We're looking for highly motivated individuals with excellent verbal and written communication skills who are ready to tackle complex challenges in AI and scientific computing.

Requirements

Pursuing a MS or PhD in Computer Science, Computer Engineering or equivalent program area.
Understanding of JAX, Autograd, tracing, code generation and DSL compilers
Working proficiency in Python, familiarity with C++
Knowledge of machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn)

Nice To Haves

Familiar with distributed systems, services, or Deep Learning at large scale
Ability to work optimally with multi-functional teams
Proven technical foundation in CPU and GPU architectures, numeric libraries, modular software design

Responsibilities

Make meaningful contributions to JAX by designing and implementing core components that drive phenomenal performance on the NVIDIA AI platform.
Increase the efficiency of teams developing JAX-based systems by developing tools that streamline the application of deep learning research and simulations to real-world products.