AI Computing Software Development Intern

NVIDIA

. Build and enhance high-performance LLM inference pipelines .

Posted 9/23/2026full-timeShanghai • ChinaEntry LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and optimizing high-performance LLM inference pipelines, with a strong focus on GPU computing, deep learning software performance, and compiler optimization techniques. Proficient in Python and C++ programming, with a solid understanding of CUDA, TensorRT, and performance profiling.

Highest-signal resume keywords

Python ProgrammingC++ ProgrammingCUDA ProgrammingTensorRT OptimizationPerformance Profiling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

LLM Inference PipelinesModel Execution OptimizationGPU Compute KernelsCompiler OptimizationMemory AllocationOperator FusionMulti-GPU Model ServingDeep Learning Software PerformancePerformance AnalysisParallel Programming

Soft Skills

Problem-SolvingCuriosity for AI SystemsPassion for GPU Computing

Tools & Technologies

TensorRTCUDALLVMMLIRNVIDIA GPUs

Industry Keywords

Computer ScienceComputer EngineeringElectrical EngineeringApplied MathematicsAI Systems

Tech Stack

Tools & technologies

PythonPyTorchC++

About the role

Key responsibilities & impact

Build and enhance high-performance LLM inference pipelines
Analyze and optimize model execution, scalability, and memory usage
Collaborate across framework and research teams to deliver efficient multi-GPU model serving
Improve TensorRT compiler graph transformations and code generation for NVIDIA GPUs
Develop compiler optimization passes
Refine operator fusion and memory allocation
Collaborate with CUDA and hardware architecture teams
Design and tune GPU compute kernels and DSL implementations for GEMM, MoE, Attention, and Convolution
Profile, analyze, and improve CUDA kernel performance to maximize GPU efficiency

Requirements

What you’ll need

Pursuing an M.S. or Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or related field
Excellent problem-solving ability
Curiosity for cutting-edge AI systems
Passion for GPU computing and deep learning software performance
TensorRT LLM track: Strong Python programming and experience with PyTorch; solid understanding of inference and GPU acceleration
TensorRT Compiler track: Proficient in C++, with experience in compiler or performance optimization
CuTe DSL & CUDA Kernels track: Skilled in C/C++ and CUDA or parallel programming; familiar with LLVM, MLIR and compiler; understanding of computer architecture and performance profiling/analysis/optimization

Benefits

Comp & perks

Diverse, supportive work environment
Opportunity to work on AI computing platforms driving innovation across industries worldwide