# Software Engineer, AI and DL Kernel Libraries

**Company**: NVIDIA
**Location**: Shanghai
**Experience**: senior
**Job type**: full-time
**Category**: Engineering
**Industry**: Technology

**Apply**: https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/China-Shanghai/Software-Engineer--AI-and-DL-Kernel-Libraries_JR2019913?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_7a29fb09-261

## Description

We're looking for outstanding AI systems software engineers to develop groundbreaking technologies across the inference systems software stack. Our team builds core AI systems software that accelerates high-impact workloads on NVIDIA GPUs, from deep learning primitives and kernel libraries to LLM inference runtimes, serving abstractions, and code generation technologies.

As a member of the team, you will help design, build, optimize, and ship production-quality software that powers NVIDIA's AI software stack.

This role spans both foundational library engineering and next-generation inference systems work, with opportunities to contribute across the stack from low-level kernels and performance primitives to serving runtimes and developer-facing abstractions.

**Responsibilities:**

- Develop production-quality software that ships as part of NVIDIA's AI software stack, including cuDNN, FlashInfer, and optimized support for large language model inference workloads.

- Innovate and develop new AI systems technologies for efficient inference, with a focus on performance, scalability, maintainability, and usability.

- Design, implement, and optimize kernels for high-impact AI workloads across LLM inference, generative AI, computer vision, autonomous driving, and recommender systems.

- Design and implement extensible software abstractions for deep learning libraries, LLM serving engines, and runtime systems.

- Build and improve just-in-time compilation, code generation, and runtime technologies for performance-critical GPU workloads.

- Analyze workload performance, tune current software, and propose improvements to future software and hardware-software interfaces.

- Collaborate closely with engineers across deep learning frameworks, libraries, kernels, compilers, and GPU architecture teams at NVIDIA.

- Contribute to open-source communities and ecosystem integrations where relevant, including projects such as FlashInfer, vLLM, and SGLang.

**Requirements:**

- Master's degree in Computer Science, Electrical Engineering, or a related field, or equivalent experience.

- 3+ years of relevant industry, research, or systems software development experience in machine learning, deep learning systems, compilers, or GPU software.

- Strong programming skills in C/C++ and Python, with hands-on experience developing high-performance software.

- Solid experience with CUDA development and GPU programming fundamentals.

- Strong experience developing or using deep learning frameworks such as PyTorch, JAX, TensorFlow, or ONNX.

- Good understanding of linear algebra, performance analysis, profiling, and code optimization.

- Experience designing software abstractions, APIs, or higher-level system architecture for performance-sensitive systems.

- Familiarity with modern machine learning and inference system trends, especially around LLMs and generative AI.

**Nice to Have:**

- Hands-on experience with inference engines and runtimes such as vLLM, SGLang, MLC, TensorRT-LLM, or similar systems.

- Background in domain-specific compiler, code generation, or library solutions for LLM inference and training.

- Expertise in machine learning compilers or IR systems such as MLIR, Apache TVM, TensorIR, or related technologies.

- Practical experience with GPU performance modeling, computer architecture, or accelerator-oriented software design.

- Open-source project ownership or meaningful contributions in deep learning systems, compilers, kernels, or inference infrastructure.

## Skills

### Required
- C/C++
- Python
- CUDA
- GPU programming
- Deep learning frameworks
- Linear algebra
- Performance analysis
- Profiling
- Code optimization

### Nice to have
- Inference engines
- Runtimes
- Domain-specific compilers
- Code generation
- Library solutions
- Machine learning compilers
- IR systems
- GPU performance modeling
- Computer architecture
- Accelerator-oriented software design
- Open-source project ownership

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Source: [Apply at nvidia.wd5.myworkdayjobs.com](https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/China-Shanghai/Software-Engineer--AI-and-DL-Kernel-Libraries_JR2019913?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)