vivoGPU系统工程师-27届暑期实习
任职要求
1、硕士及以上学历，计算机体系结构、计算机图形、微电子专业等相关专业；
2、在计算机图形学、体系结构或微电子方向理论基础扎实；具有较好的工程和编码能力；同时具备研究与创新能力；
3、优选加分项：
（1）熟悉游戏…工作职责
本岗位面向硕士及以上毕业生，旨在培养具备计算机图形学、GPU性能分析、GPU微架构设计能力与产业落地视野的高潜人才，职责涵盖图形渲染算法研究、GPU软硬件协同设计、GPU架构预研与前瞻技术探索，具体如下： 1. 针对移动端游戏的高性能渲染与能效需求，基于移动端GPU的设计特点对游戏渲染过程进行系统性分析，结合GPU软、硬件架构与游戏负载建模，识别游戏软、硬件瓶颈并制定针对性优化策略，为移动端GPU的设计迭代及游戏优化提供量化评估模型与数据支撑； 2. 面向下一代图形渲染技术演进（如实时路径追踪、神经辐射场渲染等），构建场景驱动的GPU能效评估体系，通过量化分析当前GPU软、硬件在典型负载下的PPA表现，跟踪业界最新的图形渲染技术和架构演进方向，推导出面向未来游戏场景的渲染技术和相应的GPU架构演进路径。为与供应商合作提供场景和数据支撑。
工作职责 1、万卡级 GPU 调度系统建设： 参与大规模 GPU 集群调度系统建设，围绕 Quota、优先级、抢占、弹性伸缩、碎片整理、拓扑感知调度等能力提升资源效率。 2、训推统一调度： 面向大模型训练、后训练、推理服务等不同负载，设计训推统一调度、潮汐混部、在线离线协同和资源弹性策略。 3、资源利用率治理： 建设 GPU 资源利用率分析体系，基于真实负载数据识别低效资源、资源碎片、潮汐空闲和调度瓶颈。 4、LLMOps 平台融合： 参与构建面向大模型训练、微调、推理、部署全流程的 LLMOps 能力，与云原生平台深度融合，支撑大模型生产链路稳定高效落地。 5、集群稳定性建设： 与云原生、IDC、网络、存储和业务团队协作，提升大规模 AI 集群的故障恢复能力、资源周转效率和任务稳定性。 6、前沿技术探索： 持续关注 Kubernetes、Volcano、Kueue、Ray、GPU 虚拟化、弹性调度等相关技术，探索下一代 AI 资源调度系统。
1、负责AI硬件的云端GPU和XPU的推理引擎日常研发； 2、跟进支撑智能业务，整合团队资源，协调外部团队，推动业务上线； 3、探索计算优化和芯片异构前沿技术，比如：GPU，XPU等，跟进前沿加速技术：flash attention，paged attention，MTP。 职位要求
1、万卡级 GPU 调度系统建设： 参与大规模 GPU 集群调度系统建设，围绕 Quota、优先级、抢占、弹性伸缩、碎片整理、拓扑感知调度等能力提升资源效率。 2、训推统一调度： 面向大模型训练、后训练、推理服务等不同负载，设计训推统一调度、潮汐混部、在线离线协同和资源弹性策略。 3、资源利用率治理： 建设 GPU 资源利用率分析体系，基于真实负载数据识别低效资源、资源碎片、潮汐空闲和调度瓶颈。 4、LLMOps 平台融合： 参与构建面向大模型训练、微调、推理、部署全流程的 LLMOps 能力，与云原生平台深度融合，支撑大模型生产链路稳定高效落地。 5、集群稳定性建设： 与云原生、IDC、网络、存储和业务团队协作，提升大规模 AI 集群的故障恢复能力、资源周转效率和任务稳定性。 6、前沿技术探索： 持续关注 Kubernetes、Volcano、Kueue、Ray、GPU 虚拟化、弹性调度等相关技术，探索下一代 AI 资源调度系统。
THE ROLE: As a core member of the team, you will play a pivotal role in optimizing and developing deep learning frameworks for AMD GPUs. Your strong experience will be critical in enhancing GPU kernels, deep learning models, and training/inference performance across multi-GPU and multi-node systems. You will engage with both internal GPU library teams and open-source maintainers to ensure seamless integration of optimizations, utilizing cutting-edge compiler technologies and advanced engineering principles to drive continuous improvement. THE PERSON: Highly skilled engineer with strong technical and analytical expertise in C++ development within Linux environments. The ideal candidate will thrive in both collaborative team settings and independent work, with the ability to define goals, manage development efforts, and deliver high-quality solutions. Strong problem-solving skills, a proactive approach, and a keen understanding of software engineering best practices are essential. KEY RESPONSIBILITIES: Optimize Deep Learning Frameworks: Enhance and optimize frameworks like TensorFlow and PyTorch for AMD GPUs in open-source repositories. Develop GPU Kernels: Create and optimize GPU kernels to maximize performance for specific AI operations. Develop & Optimize Models: Design and optimize deep learning models specifically for AMD GPU performance. Collaborate with GPU Library Teams: Work closely with internal teams to analyze and improve training and inference performance on AMD GPUs. Collaborate with Open-Source Maintainers: Engage with framework maintainers to ensure code changes are aligned with requirements and integrated upstream. Work in Distributed Computing Environments: Optimize deep learning performance on both scale-up (multi-GPU) and scale-out (multi-node) systems. Utilize Cutting-Edge Compiler Tech: Leverage advanced compiler technologies to improve deep learning performance. Optimize Deep Learning Pipeline: Enhance the full pipeline, including integrating graph compilers. Software Engineering Best Practices: Apply sound engineering principles to ensure robust, maintainable solutions. Mentor and Guide: Provide mentorship to junior team members, fostering growth and collaboration through code reviews, knowledge sharing, and technical guidance. PREFERRED EXPERIENCE: GPU Kernel Development & Optimization: Strong experience in designing and optimizing GPU kernels for deep learning on AMD GPUs using HIP, CUDA, and assembly (ASM). Strong knowledge of AMD architectures (GCN, RDNA) and low-level programming to maximize performance for AI operations, leveraging tools like Compute Kernel (CK), CUTLASS, and Triton for multi-GPU and multi-platform performance. Deep Learning Integration: Strong experience in integrating optimized GPU performance into machine learning frameworks (e.g., TensorFlow, PyTorch) to accelerate model training and inference, with a focus on scaling and throughput. Software Engineering: Expert skills in Python and C++, with experience in debugging, performance tuning, and test design to ensure high-quality, maintainable software solutions. High-Performance Computing: Strong experience in running large-scale workloads on heterogeneous compute clusters, optimizing for efficiency and scalability. Compiler Optimization: Sound understanding of compiler theory and tools like LLVM and ROCm for kernel and system performance optimization. ACADEMIC CREDENTIALS: Master’s and/or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field. 5+ years of professional experience in technical software development, with a focus on GPU optimization, performance engineering, and framework development. #LI-FL1