太极GPU智能调度研发工程师（深圳/北京/上海/杭州）
Tencent
太极GPU智能调度研发工程师（深圳/北京/上海/杭州）
Job Description
太极GPU智能调度研发工程师（深圳/北京/上海/杭州）
📋 Job Overview
The Taiji GPU Intelligent Scheduling R&D Engineer role focuses on leading the development and optimization of large-scale GPU cluster scheduling systems to enhance resource utilization and efficiency in AI training tasks. Responsibilities include optimizing network, storage, and compute synergies, building high-availability frameworks using cloud-native technologies, and exploring advanced areas like hybrid cloud and heterogeneous computing. This position is based in Shenzhen, Beijing, Shanghai, or Hangzhou, supporting Tencent's cutting-edge distributed training infrastructure.
📍 Location: Shenzhen, China
🏢 Business Unit: TEG
📄 Full Description
2.深入优化RDMA高速网络、分布式存储与计算资源的协同调度，有效解决大规模训练任务中的性能瓶颈，提升整体计算效率；
3.基于Kubernetes、Docker等云原生技术，构建高可用调度框架，全面支持分布式训练框架，实现任务编排、容灾与混部能力，并深入K8s调度器、CSI插件及CRD的开发，推动大规模训推技术的实际落地；
4.积极探索混合云、虚拟化、ARM异构计算等前沿方向，不断推动。
🎯 Key Responsibilities
- Lead global resource scheduling for 10,000-card GPU clusters through refined management and optimization strategies to significantly improve resource utilization and ensure efficient, stable operation of offline and online tasks.
- Deeply optimize the collaborative scheduling of RDMA high-speed networks, distributed storage, and computing resources to effectively resolve performance bottlenecks in large-scale training tasks and enhance overall computing efficiency.
- Build high-availability scheduling frameworks based on Kubernetes, Docker, and other cloud-native technologies to fully support distributed training frameworks, enabling task orchestration, disaster recovery, and co-location capabilities, while developing K8s schedulers, CSI plugins, and CRDs to drive the practical implementation of large-scale training and inference technologies.
- Actively explore frontier directions such as hybrid cloud, virtualization, and ARM heterogeneous computing to continuously advance innovations.
🛠️ Required Skills
- Expertise in GPU cluster resource scheduling and optimization
- Knowledge of RDMA networks, distributed storage, and compute resource management
- Proficiency in Kubernetes, Docker, and cloud-native technologies
- Experience with distributed training frameworks, K8s schedulers, CSI plugins, and CRDs
- Familiarity with hybrid cloud, virtualization, and ARM heterogeneous computing
Locations
- Shenzhen, China
Salary
300,000 - 600,000 CNY / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Expertise in GPU cluster resource scheduling and optimizationintermediate
- Knowledge of RDMA networks, distributed storage, and compute resource managementintermediate
- Proficiency in Kubernetes, Docker, and cloud-native technologiesintermediate
- Experience with distributed training frameworks, K8s schedulers, CSI plugins, and CRDsintermediate
- Familiarity with hybrid cloud, virtualization, and ARM heterogeneous computingintermediate
Responsibilities
- Lead global resource scheduling for 10,000-card GPU clusters through refined management and optimization strategies to significantly improve resource utilization and ensure efficient, stable operation of offline and online tasks.
- Deeply optimize the collaborative scheduling of RDMA high-speed networks, distributed storage, and computing resources to effectively resolve performance bottlenecks in large-scale training tasks and enhance overall computing efficiency.
- Build high-availability scheduling frameworks based on Kubernetes, Docker, and other cloud-native technologies to fully support distributed training frameworks, enabling task orchestration, disaster recovery, and co-location capabilities, while developing K8s schedulers, CSI plugins, and CRDs to drive the practical implementation of large-scale training and inference technologies.
- Actively explore frontier directions such as hybrid cloud, virtualization, and ARM heterogeneous computing to continuously advance innovations.
Tags & Categories
Answer 10 quick questions to check your fit for 太极GPU智能调度研发工程师（深圳/北京/上海/杭州） @ Tencent.
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太极GPU智能调度研发工程师（深圳/北京/上海/杭州）
Tencent
太极GPU智能调度研发工程师（深圳/北京/上海/杭州）
Job Description
太极GPU智能调度研发工程师（深圳/北京/上海/杭州）
📋 Job Overview
The Taiji GPU Intelligent Scheduling R&D Engineer role focuses on leading the development and optimization of large-scale GPU cluster scheduling systems to enhance resource utilization and efficiency in AI training tasks. Responsibilities include optimizing network, storage, and compute synergies, building high-availability frameworks using cloud-native technologies, and exploring advanced areas like hybrid cloud and heterogeneous computing. This position is based in Shenzhen, Beijing, Shanghai, or Hangzhou, supporting Tencent's cutting-edge distributed training infrastructure.
📍 Location: Shenzhen, China
🏢 Business Unit: TEG
📄 Full Description
2.深入优化RDMA高速网络、分布式存储与计算资源的协同调度，有效解决大规模训练任务中的性能瓶颈，提升整体计算效率；
3.基于Kubernetes、Docker等云原生技术，构建高可用调度框架，全面支持分布式训练框架，实现任务编排、容灾与混部能力，并深入K8s调度器、CSI插件及CRD的开发，推动大规模训推技术的实际落地；
4.积极探索混合云、虚拟化、ARM异构计算等前沿方向，不断推动。
🎯 Key Responsibilities
- Lead global resource scheduling for 10,000-card GPU clusters through refined management and optimization strategies to significantly improve resource utilization and ensure efficient, stable operation of offline and online tasks.
- Deeply optimize the collaborative scheduling of RDMA high-speed networks, distributed storage, and computing resources to effectively resolve performance bottlenecks in large-scale training tasks and enhance overall computing efficiency.
- Build high-availability scheduling frameworks based on Kubernetes, Docker, and other cloud-native technologies to fully support distributed training frameworks, enabling task orchestration, disaster recovery, and co-location capabilities, while developing K8s schedulers, CSI plugins, and CRDs to drive the practical implementation of large-scale training and inference technologies.
- Actively explore frontier directions such as hybrid cloud, virtualization, and ARM heterogeneous computing to continuously advance innovations.
🛠️ Required Skills
- Expertise in GPU cluster resource scheduling and optimization
- Knowledge of RDMA networks, distributed storage, and compute resource management
- Proficiency in Kubernetes, Docker, and cloud-native technologies
- Experience with distributed training frameworks, K8s schedulers, CSI plugins, and CRDs
- Familiarity with hybrid cloud, virtualization, and ARM heterogeneous computing
Locations
- Shenzhen, China
Salary
300,000 - 600,000 CNY / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Expertise in GPU cluster resource scheduling and optimizationintermediate
- Knowledge of RDMA networks, distributed storage, and compute resource managementintermediate
- Proficiency in Kubernetes, Docker, and cloud-native technologiesintermediate
- Experience with distributed training frameworks, K8s schedulers, CSI plugins, and CRDsintermediate
- Familiarity with hybrid cloud, virtualization, and ARM heterogeneous computingintermediate
Responsibilities
- Lead global resource scheduling for 10,000-card GPU clusters through refined management and optimization strategies to significantly improve resource utilization and ensure efficient, stable operation of offline and online tasks.
- Deeply optimize the collaborative scheduling of RDMA high-speed networks, distributed storage, and computing resources to effectively resolve performance bottlenecks in large-scale training tasks and enhance overall computing efficiency.
- Build high-availability scheduling frameworks based on Kubernetes, Docker, and other cloud-native technologies to fully support distributed training frameworks, enabling task orchestration, disaster recovery, and co-location capabilities, while developing K8s schedulers, CSI plugins, and CRDs to drive the practical implementation of large-scale training and inference technologies.
- Actively explore frontier directions such as hybrid cloud, virtualization, and ARM heterogeneous computing to continuously advance innovations.
Tags & Categories
Answer 10 quick questions to check your fit for 太极GPU智能调度研发工程师（深圳/北京/上海/杭州） @ Tencent.
Related Books and Jobs