游戏AI-强化学习算法研究员
Tencent
游戏AI-强化学习算法研究员
Job Description
游戏AI-强化学习算法研究员
📋 Job Overview
The role involves participating in Tencent's game AI algorithm research and application, focusing on reinforcement learning, imitation learning, meta-learning, and other techniques tailored to game scenarios to provide technical solutions. Responsibilities include optimizing reinforcement learning algorithms for better efficiency and effectiveness in game environments, as well as exploring cutting-edge technologies to advance their integration into business applications. This position is ideal for researchers passionate about advancing AI in gaming.
📍 Location: Shenzhen, China
🏢 Business Unit: TEG
📄 Full Description
2.强化学习算法应用效果优化，提高强化学习效率和效果，结合游戏场景进行应用；
3.前沿技术的探索，推进强化学习技术在业务场景的应用。
🎯 Key Responsibilities
- Participate in Tencent game AI algorithm research and application, including but not limited to reinforcement learning, imitation learning, meta-learning, etc., combined with game scenarios to provide technical solutions
- Optimize reinforcement learning algorithm application effects, improve reinforcement learning efficiency and effectiveness, and apply in combination with game scenarios
- Explore frontier technologies and promote the application of reinforcement learning technology in business scenarios
Locations
- Shenzhen, China
Salary
300,000 - 800,000 CNY / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Responsibilities
- Participate in Tencent game AI algorithm research and application, including but not limited to reinforcement learning, imitation learning, meta-learning, etc., combined with game scenarios to provide technical solutions
- Optimize reinforcement learning algorithm application effects, improve reinforcement learning efficiency and effectiveness, and apply in combination with game scenarios
- Explore frontier technologies and promote the application of reinforcement learning technology in business scenarios
Tags & Categories
游戏AI-强化学习算法研究员
Tencent
游戏AI-强化学习算法研究员
Job Description
游戏AI-强化学习算法研究员
📋 Job Overview
The role involves participating in Tencent's game AI algorithm research and application, focusing on reinforcement learning, imitation learning, meta-learning, and other techniques tailored to game scenarios to provide technical solutions. Responsibilities include optimizing reinforcement learning algorithms for better efficiency and effectiveness in game environments, as well as exploring cutting-edge technologies to advance their integration into business applications. This position is ideal for researchers passionate about advancing AI in gaming.
📍 Location: Shenzhen, China
🏢 Business Unit: TEG
📄 Full Description
2.强化学习算法应用效果优化，提高强化学习效率和效果，结合游戏场景进行应用；
3.前沿技术的探索，推进强化学习技术在业务场景的应用。
🎯 Key Responsibilities
- Participate in Tencent game AI algorithm research and application, including but not limited to reinforcement learning, imitation learning, meta-learning, etc., combined with game scenarios to provide technical solutions
- Optimize reinforcement learning algorithm application effects, improve reinforcement learning efficiency and effectiveness, and apply in combination with game scenarios
- Explore frontier technologies and promote the application of reinforcement learning technology in business scenarios
Locations
- Shenzhen, China
Salary
300,000 - 800,000 CNY / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Responsibilities
- Participate in Tencent game AI algorithm research and application, including but not limited to reinforcement learning, imitation learning, meta-learning, etc., combined with game scenarios to provide technical solutions
- Optimize reinforcement learning algorithm application effects, improve reinforcement learning efficiency and effectiveness, and apply in combination with game scenarios
- Explore frontier technologies and promote the application of reinforcement learning technology in business scenarios
Tags & Categories