混元多模态算法研究员（Omni模态）（北京/上海/北美）
Tencent
混元多模态算法研究员（Omni模态）（北京/上海/北美）
Job Description
混元多模态算法研究员（Omni模态）（北京/上海/北美）
📋 Job Overview
Tencent is seeking a Multimodal Algorithm Researcher for the Omni-modality team to advance the development of large multimodal models. The role involves designing training data, algorithms, and optimizations for pre-training, SFT, and RL, while evaluating model capabilities and exploring downstream applications. Responsibilities also include scientific analysis to resolve performance bottlenecks and pioneering new paradigms for multimodal understanding and generation to push model boundaries.
📍 Location: Shenzhen, China
🏢 Business Unit: TEG
📄 Full Description
1.从事Omni多模态大模型的研发，包括训练数据的设计和构造，基础模型算法的设计，针对预训练/SFT/RL相关的优化，模型能力的评测，各种下游应用场景的探索；
2.科学分析研发中的各种问题，找到模型性能的瓶颈，从第一性原理出发找到解决方案，加速模型的开发和迭代，确保模型的竞争力和领先性；
3.探索实现Omni模态理解、生成能力的不同范式，研究下一代的模型架构，探索多模态模型的边界。
2.科学分析研发中的各种问题，找到模型性能的瓶颈，从第一性原理出发找到解决方案，加速模型的开发和迭代，确保模型的竞争力和领先性；
3.探索实现Omni模态理解、生成能力的不同范式，研究下一代的模型架构，探索多模态模型的边界。
🎯 Key Responsibilities
- Engage in R&D of Omni multimodal large models, including training data design and construction, base model algorithm design, optimizations related to pre-training/SFT/RL, model capability evaluation, and exploration of various downstream application scenarios
- Scientifically analyze various issues in R&D, identify model performance bottlenecks, find solutions from first principles, accelerate model development and iteration, and ensure the model's competitiveness and leadership
- Explore different paradigms for achieving Omni multimodal understanding and generation capabilities, research next-generation model architectures, and push the boundaries of multimodal models
✅ Required Qualifications
- PhD or Master's degree in Computer Science, AI, or related fields
- Strong background in machine learning and deep learning
- Experience with large-scale model training and optimization
⭐ Preferred Qualifications
- Publications in top AI conferences like NeurIPS, ICML, or CVPR
- Experience with multimodal data processing and model architectures
- Familiarity with RLHF or advanced optimization techniques
🛠️ Required Skills
- Proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
- Expertise in multimodal AI, including vision, language, and audio processing
- Strong problem-solving and analytical skills from first principles
- Experience with model evaluation metrics and benchmarking
- Ability to work collaboratively in a fast-paced research environment
🎁 Benefits
- Competitive salary and equity packages
- Comprehensive health and wellness benefits
- Opportunities for professional development and research collaboration
- Flexible work locations in Beijing, Shanghai, or North America
Locations
- Shenzhen, China
Salary
Estimated Salary Rangemedium confidence
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
- Proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)intermediate
- Expertise in multimodal AI, including vision, language, and audio processingintermediate
- Strong problem-solving and analytical skills from first principlesintermediate
- Experience with model evaluation metrics and benchmarkingintermediate
- Ability to work collaboratively in a fast-paced research environmentintermediate
Required Qualifications
- PhD or Master's degree in Computer Science, AI, or related fields (experience)
- Strong background in machine learning and deep learning (experience)
- Experience with large-scale model training and optimization (experience)
Preferred Qualifications
- Publications in top AI conferences like NeurIPS, ICML, or CVPR (experience)
- Experience with multimodal data processing and model architectures (experience)
- Familiarity with RLHF or advanced optimization techniques (experience)
Responsibilities
- Engage in R&D of Omni multimodal large models, including training data design and construction, base model algorithm design, optimizations related to pre-training/SFT/RL, model capability evaluation, and exploration of various downstream application scenarios
- Scientifically analyze various issues in R&D, identify model performance bottlenecks, find solutions from first principles, accelerate model development and iteration, and ensure the model's competitiveness and leadership
- Explore different paradigms for achieving Omni multimodal understanding and generation capabilities, research next-generation model architectures, and push the boundaries of multimodal models
Benefits
- general: Competitive salary and equity packages
- general: Comprehensive health and wellness benefits
- general: Opportunities for professional development and research collaboration
- general: Flexible work locations in Beijing, Shanghai, or North America
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Answer 10 quick questions to check your fit for 混元多模态算法研究员（Omni模态）（北京/上海/北美） @ Tencent.
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混元多模态算法研究员（Omni模态）（北京/上海/北美）
Tencent
混元多模态算法研究员（Omni模态）（北京/上海/北美）
Job Description
混元多模态算法研究员（Omni模态）（北京/上海/北美）
📋 Job Overview
Tencent is seeking a Multimodal Algorithm Researcher for the Omni-modality team to advance the development of large multimodal models. The role involves designing training data, algorithms, and optimizations for pre-training, SFT, and RL, while evaluating model capabilities and exploring downstream applications. Responsibilities also include scientific analysis to resolve performance bottlenecks and pioneering new paradigms for multimodal understanding and generation to push model boundaries.
📍 Location: Shenzhen, China
🏢 Business Unit: TEG
📄 Full Description
1.从事Omni多模态大模型的研发，包括训练数据的设计和构造，基础模型算法的设计，针对预训练/SFT/RL相关的优化，模型能力的评测，各种下游应用场景的探索；
2.科学分析研发中的各种问题，找到模型性能的瓶颈，从第一性原理出发找到解决方案，加速模型的开发和迭代，确保模型的竞争力和领先性；
3.探索实现Omni模态理解、生成能力的不同范式，研究下一代的模型架构，探索多模态模型的边界。
2.科学分析研发中的各种问题，找到模型性能的瓶颈，从第一性原理出发找到解决方案，加速模型的开发和迭代，确保模型的竞争力和领先性；
3.探索实现Omni模态理解、生成能力的不同范式，研究下一代的模型架构，探索多模态模型的边界。
🎯 Key Responsibilities
- Engage in R&D of Omni multimodal large models, including training data design and construction, base model algorithm design, optimizations related to pre-training/SFT/RL, model capability evaluation, and exploration of various downstream application scenarios
- Scientifically analyze various issues in R&D, identify model performance bottlenecks, find solutions from first principles, accelerate model development and iteration, and ensure the model's competitiveness and leadership
- Explore different paradigms for achieving Omni multimodal understanding and generation capabilities, research next-generation model architectures, and push the boundaries of multimodal models
✅ Required Qualifications
- PhD or Master's degree in Computer Science, AI, or related fields
- Strong background in machine learning and deep learning
- Experience with large-scale model training and optimization
⭐ Preferred Qualifications
- Publications in top AI conferences like NeurIPS, ICML, or CVPR
- Experience with multimodal data processing and model architectures
- Familiarity with RLHF or advanced optimization techniques
🛠️ Required Skills
- Proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
- Expertise in multimodal AI, including vision, language, and audio processing
- Strong problem-solving and analytical skills from first principles
- Experience with model evaluation metrics and benchmarking
- Ability to work collaboratively in a fast-paced research environment
🎁 Benefits
- Competitive salary and equity packages
- Comprehensive health and wellness benefits
- Opportunities for professional development and research collaboration
- Flexible work locations in Beijing, Shanghai, or North America
Locations
- Shenzhen, China
Salary
Estimated Salary Rangemedium confidence
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
- Proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)intermediate
- Expertise in multimodal AI, including vision, language, and audio processingintermediate
- Strong problem-solving and analytical skills from first principlesintermediate
- Experience with model evaluation metrics and benchmarkingintermediate
- Ability to work collaboratively in a fast-paced research environmentintermediate
Required Qualifications
- PhD or Master's degree in Computer Science, AI, or related fields (experience)
- Strong background in machine learning and deep learning (experience)
- Experience with large-scale model training and optimization (experience)
Preferred Qualifications
- Publications in top AI conferences like NeurIPS, ICML, or CVPR (experience)
- Experience with multimodal data processing and model architectures (experience)
- Familiarity with RLHF or advanced optimization techniques (experience)
Responsibilities
- Engage in R&D of Omni multimodal large models, including training data design and construction, base model algorithm design, optimizations related to pre-training/SFT/RL, model capability evaluation, and exploration of various downstream application scenarios
- Scientifically analyze various issues in R&D, identify model performance bottlenecks, find solutions from first principles, accelerate model development and iteration, and ensure the model's competitiveness and leadership
- Explore different paradigms for achieving Omni multimodal understanding and generation capabilities, research next-generation model architectures, and push the boundaries of multimodal models
Benefits
- general: Competitive salary and equity packages
- general: Comprehensive health and wellness benefits
- general: Opportunities for professional development and research collaboration
- general: Flexible work locations in Beijing, Shanghai, or North America
Tags & Categories
Answer 10 quick questions to check your fit for 混元多模态算法研究员（Omni模态）（北京/上海/北美） @ Tencent.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs