Differentiable Simulation Research Intern - Summer 2027

at Nvidia

USD 38-94 per hour

INTERN

✅ On-site

Tech Stack
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AI Algorithms @ 6 CUDA @ 3 GPU @ 2 JAX @ 3 Machine Learning Mathematics @ 3 Performance Analysis @ 3 Profiling @ 3 PyTorch @ 3 Python @ 2

Details

NVIDIA is seeking a research intern to work on scalable differentiable algorithms, including adjoint and automatic differentiation methods. The research will connect simulations with experimental data for calibration and data assimilation using AI/ML methods. Approaches will be evaluated on simulation workloads and assessed for integration into NVIDIA software used by researchers, engineers, and partner developers.

Responsibilities

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.

Requirements

Pursuing a PhD in applied mathematics, scientific computing, computational engineering, computer science, mechanical engineering, aerospace engineering, or an 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.

Preferred Qualifications

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.

Benefits

Interns are eligible for NVIDIA intern benefits.

Applications will be accepted at least until October 16, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer.