Artificial Intelligence

• Develop high performance operators on NVIDIA GPUs for cuBLAS, TensorRT, cuDNN, cuSparse and cuTensor libraries
• Analyze the performance of various GPU kernels on existing/new architecture
• Identify bottlenecks and propose creative solutions to improve GPU kernel performance
• Design and develop software for kernel authoring and shipping
• Adopt cutting-edge AI technologies in GPU kernel or similar development workflow
• Explore computer architectures for deep learning and work at the intersection of hardware and software
• Continuously explore and optimize the performance of operators and fusion operators in deep learning networks
• Design and develop scalable modular infrastructure for optimized operators used in training and inference

AI Computing Software Intern, GPU Kernel Libraries

• 负责车端故障事件检测与数据自动切片、回传工具开发，支持感知、定位、规划、控制等节点故障信号触发。
• 建设企业级自动驾驶数据闭环平台，统一管理多矿山、多车辆、多版本回传数据，并打通数据标注平台。
• 负责基于 VLM 的自动化问题诊断能力开发，将图像、点云、轨迹、日志、故障码等多模态信息组织为模型上下文，自动输出故障现象、根因分析与排查建议。
• 结合 RAG、工具调用或 Agent 工作流完成复杂诊断，并建立证据引用与人工确认机制以控制模型幻觉。
• 负责 VLM 领域微调与评估体系建设（SFT、LoRA/QLoRA），持续沉淀现场数据实现闭环迭代。
• 建设 MLOps Pipeline，支持感知、融合等模型的一键训练、验证、导出（ONNX/TensorRT）与车端部署。
• 负责平台架构设计与核心开发，保障系统可扩展、高可用、可观测。

AI Development Engineer

• Develop and optimize training infrastructure for advanced Physical AI world models, supporting pre-training, supervised fine-tuning (SFT), and reinforcement learning (RL)
• Explore distributed parallelism, sharding, low-precision training, compute–communication overlap, numerical consistency, and efficient weight synchronization between training and inference
• Build Physical AI post-training and RL infrastructure supporting advanced training algorithms
• Connect simulation or real-robot interaction with experience collection, rollout inference, reward computation, training, and evaluation
• Optimize workflows through partitioning, pipelining, data transfer, and synchronization across synchronous, asynchronous, or disaggregated execution
• Improve efficiency and scalability across training, inference, simulation, and evaluation through scheduling, placement, dynamic resource allocation, and load balancing
• Support heterogeneous resources, elasticity, and fault recovery
• Analyze and optimize system performance with researchers
• Investigate, support, and compare emerging Physical AI models, training workflows, and algorithms from a systems perspective
• Use profiling, benchmarking, and performance modeling to identify bottlenecks and measure throughput, latency, GPU utilization, and policy freshness
• Share findings through tested code, documentation, and technical presentations
• Contribute to research publications where appropriate

Artificial Intelligence jobs in China

AI Computing Software Intern, GPU Kernel Libraries

AI Development Engineer

AI Infrastructure and Frameworks Intern