Differentiable Simulation Research Intern - Summer 2027

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 an inclusive, 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.  

Research in this internship will focus on scalable differentiable algorithms, including adjoint and automatic differentiation methods. The work will connect simulations with experimental data for calibration and data assimilation using AI/ML methods. These approaches will be evaluated on simulation workloads and assessed for integration into NVIDIA software used by researchers, engineers, and partner developers. If you enjoy working across mathematics, code, hardware, and engineering applications, we would love to meet!

• →Prototype GPU-native solvers with adjoint and automatic differentiation techniques for engineering simulations.
• →Integrate AI-based methods with simulation algorithms for parameter calibration, inverse problems, and data assimilation using experimental or simulation data.
• →Co-design numerical algorithms and differentiation strategies for GPU parallelism, memory hierarchy, mixed precision, reduced synchronization, and distributed multi-GPU execution.
• →Evaluate numerical accuracy, convergence, gradient quality, performance, and scalability on workloads in CFD, mechanics, thermal systems, and multiphysics.
• →Identify promising methods and help move them toward reusable capabilities in CUDA-X libraries.

• Pursuing a PhD in applied mathematics, scientific computing, computational engineering, computer science, mechanical engineering, aerospace engineering, or equivalent program area. 
• Strong foundations in algorithms related to PDEs, numerical linear algebra, iterative solvers, optimization, or scientific computing.
• Research experience with adjoint methods, automatic differentiation, inverse problems, data assimilation, gradient-aware simulation approaches, or a related area.
• Experience writing numerical software in Python and C++, with familiarity with parallel or GPU computing.
• Ability to design computational experiments, evaluate numerical and performance tradeoffs, and communicate technical results clearly.

• Experience developing, scaling, or analyzing GPU solvers, including multi-GPU or distributed execution.
• Hands-on CUDA experience and use of profiling, benchmarking, or performance analysis to improve numerical algorithms.
• Experience calibrating computational models or assimilating experimental data in scientific or industrial applications.
• Experience with NVIDIA Warp, CUDA-X libraries, PyTorch, JAX, PETSc, Trilinos, MFEM, libCEED, OpenFOAM, or related computational science frameworks.
• Publications, open-source contributions, or deployed research software in numerical methods, scientific computing, differentiable simulation, or computational engineering.

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you! 

You will also be eligible for Intern benefits. ​

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.