NVIDIA
Differentiable Simulation Research Intern - Summer 2027
US, CA, Santa Clara · Internship · Posted Oct 12, 2026
- Education
- Pursuing a PhD in applied mathematics, scientific computing, computational engineering, computer science, mechanical engineering, aerospace engineering, or equivalent program area.
About the job
Develop and evaluate GPU-native differentiable simulation methods that combine engineering simulations with AI/ML for calibration, inverse problems, and data assimilation.
What you'll do
- Prototype GPU-native solvers using adjoint and automatic differentiation methods.
- Integrate AI-based methods into simulation algorithms and assess numerical quality, performance, and scalability.
- Identify promising methods for reusable capabilities in CUDA-X libraries.
What they ask for
- Pursuing a PhD in an applicable field or equivalent program area.
- Strong foundations in PDE algorithms, numerical linear algebra, iterative solvers, optimization, or scientific computing.
- Research experience with adjoint methods, automatic differentiation, inverse problems, data assimilation, gradient-aware simulation, or a related area.
- Experience writing numerical software in Python and C++, with familiarity with parallel or GPU computing.
Nice to have
- Experience developing or analyzing GPU solvers, including multi-GPU or distributed execution; hands-on CUDA and performance profiling experience.
- Experience calibrating computational models or assimilating experimental data; familiarity with listed computational science frameworks.
Pay
38 USD - 94 USD per hour (hourly rate)
Tools
Python, C++, CUDA, CUDA-X libraries, NVIDIA Warp, PyTorch, JAX, PETSc
AI focus
Use AI/ML methods with simulations for parameter calibration, inverse problems, and data assimilation; NVIDIA also states it uses AI tools in recruiting.
Model-written summary · the original posting is authoritative