# Intern - Research in Scalable Differentiable Algorithms

**Company**: NVIDIA
**Job type**: internship
**Salary**: $38-$94 per hour
**Category**: Engineering
**Industry**: Technology

**Apply**: https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Differentiable-Simulation-Research-Intern---Summer-2027_JR2027561?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_d104da50-506

## Description

NVIDIA is seeking a talented intern to join their team in researching scalable differentiable algorithms. The successful candidate will work on developing GPU-native solvers with adjoint and automatic differentiation techniques for engineering simulations.

The internship will focus on connecting simulations with experimental data for calibration and data assimilation using AI/ML methods. The ideal candidate will have a strong foundation in algorithms related to PDEs, numerical linear algebra, iterative solvers, optimization, or scientific computing.

**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 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.

**Benefits:**

- Competitive hourly rate: $38-$94 per hour

- Eligibility for intern benefits

**How to Stand Out:**

- 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.

## Skills

### Required
- Python
- C++
- parallel computing
- GPU computing
- numerical linear algebra
- iterative solvers
- optimization
- scientific computing

### Nice to have
- CUDA
- PyTorch
- JAX
- PETSc
- Trilinos
- MFEM
- libCEED
- OpenFOAM

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Source: [Apply at nvidia.wd5.myworkdayjobs.com](https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Differentiable-Simulation-Research-Intern---Summer-2027_JR2027561?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)