About the role#
This internship focuses on scalable differentiable algorithms, specifically adjoint and automatic differentiation methods. You will work on connecting simulations with experimental data for calibration and data assimilation using AI and machine learning. Your work will be evaluated on simulation workloads and assessed for integration into NVIDIA software used by researchers and engineers.
What you'll do#
- Prototype GPU-native solvers using adjoint and automatic differentiation techniques for engineering simulations.
- Integrate AI-based methods with simulation algorithms for parameter calibration, inverse problems, and data assimilation.
- Co-design numerical algorithms and differentiation strategies for GPU parallelism, memory hierarchy, mixed precision, and distributed multi-GPU execution.
- Evaluate numerical accuracy, convergence, gradient quality, performance, and scalability across CFD, mechanics, thermal systems, and multiphysics workloads.
- Identify promising methods and help transition them into reusable capabilities within CUDA-X libraries.
What you'll need#
- Currently pursuing a PhD in applied mathematics, scientific computing, computational engineering, computer science, mechanical engineering, aerospace engineering, or a related program.
- Strong foundation 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, or gradient-aware simulation approaches.
- Experience writing numerical software in Python and C++.
- Familiarity with parallel or GPU computing.
- Ability to design computational experiments, evaluate performance tradeoffs, and communicate technical results clearly.
- Bonus points for experience with GPU solvers, CUDA, profiling tools, model calibration, or computational frameworks like NVIDIA Warp, PyTorch, JAX, PETSc, or OpenFOAM.
- A record of publications, open-source contributions, or deployed research software is preferred.
Location & details#
- Location: Santa Clara, California
- Term: Summer 2027
- Modality: On-site
- Employment: Full time
- Compensation: 38 USD to 94 USD per hour
- Application deadline: November 3, 2026
About NVIDIA
NVIDIA operates as a computer hardware manufacturer based in Santa Clara, California. Founded in 1993, the company focuses on accelerated computing and graphics technology. It produces hardware for markets including artificial intelligence, gaming, and data centers. The organization employs over 50,000 people and maintains a global presence.
How to get in at NVIDIA
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