Differentiable Simulation Research Intern - Summer 2027

NVIDIA is hiring a Differentiable Simulation Research Intern - Summer 2027 in Santa Clara, United States. It pays $38-$94/hr and Level rates it ; you can apply on Level.

AI in this role

Research intern focusing on scalable differentiable algorithms and combining simulations with AI/ML methods on NVIDIA GPUs.

pytorchjaxpythonccuda

differentiable-simulationautomatic-differentiationmachine-learninggpu-computingscientific-computing

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.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

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.

How we rate this

Differentiable Simulation Research Intern - Summer 2027 at NVIDIA rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Key facts

Differentiable Simulation Research Intern - Summer 2027 is an open job at NVIDIA.
Level rates this role AI Level 4 of 4: Builds AI.
The listed pay is $38-$94/hr.
The role is based in US, CA, Santa Clara.
The job was posted on October 12, 2026.
Seniority: intern.
Applications are handled by NVIDIA. Apply on NVIDIA

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Differentiable SimulationAutomatic DifferentiationMachine learningGPU ComputingScientific ComputingPyTorchJaxPython

Questions you could be asked

Tell me about a project where differentiable simulation was part of your work. What did you do?
Tell me about a project where automatic differentiation was part of your work. What did you do?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where gpu computing was part of your work. What did you do?
Tell me about a project where scientific computing was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Differentiable Simulation, Automatic Differentiation, Machine learning, GPU Computing, and Scientific Computing. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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