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.
What we need to see
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. More experience is expected for senior-level candidates.
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.
For senior candidates, strong experience in GPU kernel development and performance optimization, especially using CUDA C/C++, cuTile, Triton, or similar technologies, is expected.
Ways to stand out from the crowd
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.
