混元机器学习平台开发工程师（北京/深圳）
Tencent
混元机器学习平台开发工程师（北京/深圳）
Job Description
混元机器学习平台开发工程师（北京/深圳）
📋 Job Overview
The role involves designing and developing Tencent's Hunyuan Machine Learning Platform in Beijing or Shenzhen. Responsibilities include optimizing performance for large-scale training, tracking industry AIGC trends to enhance platform usability, and advancing LLMOps capabilities. The position focuses on improving internal AI technologies and architecture to lower barriers for large model development.
📍 Location: Shenzhen, China
🏢 Business Unit: TEG
📄 Full Description
2.深入理解跟踪业界AIGC动态，优化平台技术方案，提升平台易用性，降低大模型研发门槛，不断推进平台的LLMOps能力升级；
3.积极追踪业内AI动态，优化内部技术方案，改进产品性能，不断推进AI架构升级。
🎯 Key Responsibilities
- Responsible for the design and development of Tencent's Hunyuan Machine Learning Platform, including performance optimization and continuous improvement of training performance, such as multi-machine multi-card large-scale training optimization and data exchange optimization.
- Deeply understand and track industry AIGC dynamics, optimize platform technical solutions, enhance platform usability, lower the threshold for large model R&D, and continuously advance the platform's LLMOps capability upgrades.
- Actively track industry AI dynamics, optimize internal technical solutions, improve product performance, and continuously advance AI architecture upgrades.
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.
Responsibilities
- Responsible for the design and development of Tencent's Hunyuan Machine Learning Platform, including performance optimization and continuous improvement of training performance, such as multi-machine multi-card large-scale training optimization and data exchange optimization.
- Deeply understand and track industry AIGC dynamics, optimize platform technical solutions, enhance platform usability, lower the threshold for large model R&D, and continuously advance the platform's LLMOps capability upgrades.
- Actively track industry AI dynamics, optimize internal technical solutions, improve product performance, and continuously advance AI architecture upgrades.
Tags & Categories
混元机器学习平台开发工程师（北京/深圳）
Tencent
混元机器学习平台开发工程师（北京/深圳）
Job Description
混元机器学习平台开发工程师（北京/深圳）
📋 Job Overview
The role involves designing and developing Tencent's Hunyuan Machine Learning Platform in Beijing or Shenzhen. Responsibilities include optimizing performance for large-scale training, tracking industry AIGC trends to enhance platform usability, and advancing LLMOps capabilities. The position focuses on improving internal AI technologies and architecture to lower barriers for large model development.
📍 Location: Shenzhen, China
🏢 Business Unit: TEG
📄 Full Description
2.深入理解跟踪业界AIGC动态，优化平台技术方案，提升平台易用性，降低大模型研发门槛，不断推进平台的LLMOps能力升级；
3.积极追踪业内AI动态，优化内部技术方案，改进产品性能，不断推进AI架构升级。
🎯 Key Responsibilities
- Responsible for the design and development of Tencent's Hunyuan Machine Learning Platform, including performance optimization and continuous improvement of training performance, such as multi-machine multi-card large-scale training optimization and data exchange optimization.
- Deeply understand and track industry AIGC dynamics, optimize platform technical solutions, enhance platform usability, lower the threshold for large model R&D, and continuously advance the platform's LLMOps capability upgrades.
- Actively track industry AI dynamics, optimize internal technical solutions, improve product performance, and continuously advance AI architecture upgrades.
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.
Responsibilities
- Responsible for the design and development of Tencent's Hunyuan Machine Learning Platform, including performance optimization and continuous improvement of training performance, such as multi-machine multi-card large-scale training optimization and data exchange optimization.
- Deeply understand and track industry AIGC dynamics, optimize platform technical solutions, enhance platform usability, lower the threshold for large model R&D, and continuously advance the platform's LLMOps capability upgrades.
- Actively track industry AI dynamics, optimize internal technical solutions, improve product performance, and continuously advance AI architecture upgrades.
Tags & Categories