2 months ago
Shanghai, ChinaSenior
Responsibilities
- Design and prototype scalable systems for distributed AI training and inference optimization.
- Develop and evaluate enhancements to NCCL, UCX, and UCC communication libraries.
- Collaborate with TensorFlow, PyTorch, and JAX teams to improve communication-backend integration, performance, and reliability.
- Co-design GPU, DPU, and interconnect hardware features for data movement and inference/model serving.
- Advance runtime systems, communication libraries, and AI-specific protocol layers.
- Work with customers to understand requirements and provide innovative technical solutions.
Requirements
- Bachelor's, master's, or Ph.D. degree in computer science, computer engineering, electrical engineering, or a closely related field.
- At least five years of experience with DNNs, DNN scaling, parallelism of DNN frameworks, or deep learning training workloads.
- Deep understanding of inference and training workloads and optimizations, including Prefill/Decode, data parallelism, tensor parallelism, and FDSP.
- Experience with AI network parallelism using collective libraries and RDMA/RoCE.
- Background in algorithm design, system programming, and computer architecture.
- Strong programming and software development skills.
- Ability to communicate and collaborate effectively in a multinational, multi-time-zone corporate environment.
- Experience designing HPC communication middleware, including RoCE and DPUs, is preferred.
- CUDA programming experience and familiarity with NVIDIA GPUs and programming models for emerging architectures are preferred.
Benefits
- Hybrid work arrangement.
- Equal opportunity employment in a diverse work environment.
Tech Stack
PyTorchTensorFlow
Categories
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.