Differentiable Simulation Research Intern - Summer 2027

NVIDIA Corporation; Santa Clara (CA); On-site; USD 52,000 - 129,000; Full time

Benefits offered by this job

Intern benefits

Job summary

NVIDIA Corporation is seeking a PhD candidate for an internship focused on scalable differentiable algorithms. Work spans mathematics, code, hardware, and engineering applications, with emphasis on adjoint and automatic differentiation methods in GPU-accelerated simulations.

You will prototype GPU-native solvers, integrate AI-based methods with simulations, and evaluate accuracy, convergence, and scalability across CFD, mechanics, and multiphysics workloads.

Qualifications

• Pursuing a PhD in applied mathematics, scientific computing, computational engineering, CS, or related field.
• Strong foundations in PDEs, numerical linear algebra, iterative solvers, optimization, or scientific computing.
• Experience with adjoint methods, automatic differentiation, inverse problems, or data assimilation.
• Experience writing numerical software in Python and C++, with GPU computing experience.
• Ability to design experiments, compare numerical tradeoffs, and communicate results clearly.

Responsibilities

• Prototype GPU-native solvers with adjoint and autodiff techniques for engineering simulations.
• Integrate AI-based methods with simulations for parameter calibration and data assimilation.
• Co-design algorithms for GPU parallelism, memory, mixed precision, and multi-GPU execution.
• Evaluate numerical accuracy, convergence, gradient quality, and scalability on CFD and multiphysics workloads.
• Move promising methods toward reusable capabilities in CUDA-X libraries.

Skills

Python

C++

CUDA

GPU Computing

Numerical Methods

Education

PhD candidate in applied mathematics / scientific computing

Tools

CUDA-X libraries

PyTorch

JAX

OpenFOAM

Job description

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!

What you'll be doing:

• 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.

What we need to see:

• 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.

Ways to stand out from the crowd:

• 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!

Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The hourly rate for our interns is 38 USD - 94 USD. You will also be eligible for Intern benefits.

Applications for this job will be accepted at least until October 16, 2026.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

NVIDIA pioneered accelerated computing. Today, our AI infrastructure powers global intelligence, transforming every industry. Learn more about NVIDIA.