腾讯游戏-资深机器学习工程师/专家-大规模模型训练与推理优化
Tencent
腾讯游戏-资深机器学习工程师/专家-大规模模型训练与推理优化
Job Description
腾讯游戏-资深机器学习工程师/专家-大规模模型训练与推理优化
📋 Job Overview
The Senior Machine Learning Engineer/Expert role at Tencent Games focuses on optimizing distributed training and inference systems for large-scale models in 3D/animation asset generation. Responsibilities include implementing parallel strategies to address computational challenges like memory bottlenecks and communication delays. The position also involves developing platforms for multi-modal RLHF training and inference.
📍 Location: Shenzhen, China
🏢 Business Unit: IEG
📄 Full Description
2.通过数据并行、模型并行、流水线并行、专家并行等策略的工程实现和性能优化；
3.解决大模型训练中的显存瓶颈、通信延迟和负载均衡等核心计算资源问题；
4.负责多模态RLHF训练与推理平台的实现。
🎯 Key Responsibilities
- Responsible for performance optimization of distributed training and inference systems for large models in 3D/animation asset generation
- Engineering implementation and performance optimization of strategies such as data parallelism, model parallelism, pipeline parallelism, and expert parallelism
- Solving core computational resource issues in large model training, including memory bottlenecks, communication latency, and load balancing
- Responsible for the implementation of multi-modal RLHF training and inference platforms
🛠️ Required Skills
- Expertise in distributed training and inference for large-scale machine learning models
- Knowledge of parallelism strategies (data, model, pipeline, expert)
- Skills in optimizing computational resources for AI training (memory, communication, load balancing)
- Experience with multi-modal RLHF platforms
Locations
- Shenzhen, China
Salary
800,000 - 1,500,000 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 distributed training and inference for large-scale machine learning modelsintermediate
- Knowledge of parallelism strategies (data, model, pipeline, expert)intermediate
- Skills in optimizing computational resources for AI training (memory, communication, load balancing)intermediate
- Experience with multi-modal RLHF platformsintermediate
Responsibilities
- Responsible for performance optimization of distributed training and inference systems for large models in 3D/animation asset generation
- Engineering implementation and performance optimization of strategies such as data parallelism, model parallelism, pipeline parallelism, and expert parallelism
- Solving core computational resource issues in large model training, including memory bottlenecks, communication latency, and load balancing
- Responsible for the implementation of multi-modal RLHF training and inference platforms
Tags & Categories
Answer 10 quick questions to check your fit for 腾讯游戏-资深机器学习工程师/专家-大规模模型训练与推理优化 @ Tencent.
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腾讯游戏-资深机器学习工程师/专家-大规模模型训练与推理优化
Tencent
腾讯游戏-资深机器学习工程师/专家-大规模模型训练与推理优化
Job Description
腾讯游戏-资深机器学习工程师/专家-大规模模型训练与推理优化
📋 Job Overview
The Senior Machine Learning Engineer/Expert role at Tencent Games focuses on optimizing distributed training and inference systems for large-scale models in 3D/animation asset generation. Responsibilities include implementing parallel strategies to address computational challenges like memory bottlenecks and communication delays. The position also involves developing platforms for multi-modal RLHF training and inference.
📍 Location: Shenzhen, China
🏢 Business Unit: IEG
📄 Full Description
2.通过数据并行、模型并行、流水线并行、专家并行等策略的工程实现和性能优化；
3.解决大模型训练中的显存瓶颈、通信延迟和负载均衡等核心计算资源问题；
4.负责多模态RLHF训练与推理平台的实现。
🎯 Key Responsibilities
- Responsible for performance optimization of distributed training and inference systems for large models in 3D/animation asset generation
- Engineering implementation and performance optimization of strategies such as data parallelism, model parallelism, pipeline parallelism, and expert parallelism
- Solving core computational resource issues in large model training, including memory bottlenecks, communication latency, and load balancing
- Responsible for the implementation of multi-modal RLHF training and inference platforms
🛠️ Required Skills
- Expertise in distributed training and inference for large-scale machine learning models
- Knowledge of parallelism strategies (data, model, pipeline, expert)
- Skills in optimizing computational resources for AI training (memory, communication, load balancing)
- Experience with multi-modal RLHF platforms
Locations
- Shenzhen, China
Salary
800,000 - 1,500,000 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 distributed training and inference for large-scale machine learning modelsintermediate
- Knowledge of parallelism strategies (data, model, pipeline, expert)intermediate
- Skills in optimizing computational resources for AI training (memory, communication, load balancing)intermediate
- Experience with multi-modal RLHF platformsintermediate
Responsibilities
- Responsible for performance optimization of distributed training and inference systems for large models in 3D/animation asset generation
- Engineering implementation and performance optimization of strategies such as data parallelism, model parallelism, pipeline parallelism, and expert parallelism
- Solving core computational resource issues in large model training, including memory bottlenecks, communication latency, and load balancing
- Responsible for the implementation of multi-modal RLHF training and inference platforms
Tags & Categories
Answer 10 quick questions to check your fit for 腾讯游戏-资深机器学习工程师/专家-大规模模型训练与推理优化 @ Tencent.
Related Books and Jobs