Software Engineer, DL Kernel Libraries - New College Grad 2027

This internship role supports NVIDIA’s DL Kernel Libraries team by building software stack components for deep learning inference and training, spanning GPU kernel technologies, libraries, and code generation efforts. The work emphasizes practical performance and extensibility across modern AI workloads.

Role Overview

As a Software Engineer Intern in the DL Kernel Libraries group, you will contribute to inference and training systems software, including kernel implementations and compiler or runtime components. Focus areas include cuDNN, FlashInfer, efficient kernel engineering, and domain-specific tooling that accelerates AI workloads.

Key Responsibilities

• Innovate and develop new AI systems technologies aimed at efficient inference and training
• Design, implement, and optimize kernels for high-impact AI workloads
• Design and implement extensible abstractions for serving engines and training frameworks
• Build efficient just-in-time domain specific compilers and runtimes
• Collaborate closely with engineers across NVIDIA spanning deep learning frameworks, libraries, kernels, and GPU architecture teams
• Contribute to open source communities across FlashInfer, cuDNN, vLLM, SGLang, PyTorch, JAX, and XLA

Required Qualifications

• BS, MS, or PhD degree in Computer Science, Electrical Engineering, or a related field, or equivalent experience
• Strong experience in developing or using deep learning frameworks (such as PyTorch, JAX, TensorFlow, ONNX) or inference engines and runtimes such as vLLM, SGLang, and MLC
• Strong Python and C/C++ programming skills

Technologies and Tools

• PyTorch, JAX, TensorFlow, ONNX
• vLLM, SGLang, MLC
• Python, C/C++
• FlashInfer, cuDNN
• CUDA C/C++, CUTLASS DSL, cuTile, Triton
• Flash Attention
• Apache TVM, MLIR
• CUDA, XLA

Salary and Location

• Location: Santa Clara, CA (onsite)
• Compensation: USD 108,000 to 195,500 per year
• Job Type: Internship

Benefits

You will also be eligible for equity and benefits.

What Helps You Stand Out

• Background in domain specific compiler and library solutions for LLM inference and training, such as FlashInfer and Flash Attention
• Expertise in training frameworks and inference engines
• Expertise in machine learning compilers, such as Apache TVM and MLIR
• Strong experience in GPU kernel development and performance optimizations, including CUDA C/C++, CUTLASS DSL, cuTile, Triton, or similar approaches
• Open source project ownership or contributions