腾讯广告-大模型推荐算法工程师
Tencent
腾讯广告-大模型推荐算法工程师
Job Description
腾讯广告-大模型推荐算法工程师
📋 Job Overview
The role focuses on advancing recommendation algorithms for Tencent Advertising using large models, including foundational large models, model scaling up, and multimodal recommendations. Responsibilities include building and optimizing base large models with billions of samples and features, exploring scaling laws, integrating multimodal technologies, and staying updated with AI advancements to enhance ad performance metrics like CTR and CVR. This position drives continuous breakthroughs in modeling techniques to improve advertising effectiveness.
📍 Location: Shenzhen, China
🏢 Business Unit: CDG
📄 Full Description
1.负责广告基础推荐算法方向，如基础大模型、模型 scaling up、多模态推荐等方向，通过千亿样本、特征，结合模型 scaling up和多模态技术，持续推动建模技术升级突破；
2.基础大模型建模和优化，基于千亿样本、特征，构建基座大模型，并研发Embedding迁移框架及模型蒸馏等技术，实现基座大模型能力向推荐系统的有效迁移，驱动广告CTR/CVR等核心指标持续提升；
3.通过特征、样本、模型的scaling up，持续探索模型scaling law 的天花板；
4.将多模态技术融入广告推荐建模中，通过更丰富信息、更泛化的表达，持续提升模型效果；
5.积极跟进AI学术界和业界的最新动态，优化内部技术方案，不断推进广告算法设计升级。
2.基础大模型建模和优化，基于千亿样本、特征，构建基座大模型，并研发Embedding迁移框架及模型蒸馏等技术，实现基座大模型能力向推荐系统的有效迁移，驱动广告CTR/CVR等核心指标持续提升；
3.通过特征、样本、模型的scaling up，持续探索模型scaling law 的天花板；
4.将多模态技术融入广告推荐建模中，通过更丰富信息、更泛化的表达，持续提升模型效果；
5.积极跟进AI学术界和业界的最新动态，优化内部技术方案，不断推进广告算法设计升级。
🎯 Key Responsibilities
- Responsible for foundational recommendation algorithm directions such as base large models, model scaling up, multimodal recommendations, etc., using billions of samples and features, combined with model scaling up and multimodal technologies to continuously promote modeling technology upgrades and breakthroughs.
- Base large model modeling and optimization, building base large models based on billions of samples and features, and developing Embedding migration frameworks and model distillation technologies to effectively migrate base large model capabilities to recommendation systems, driving continuous improvement in core metrics like ad CTR/CVR.
- Through scaling up of features, samples, and models, continuously explore the ceiling of model scaling laws.
- Incorporate multimodal technologies into ad recommendation modeling, using richer information and more generalized expressions to continuously improve model performance.
- Actively follow the latest developments in AI academia and industry, optimize internal technical solutions, and continuously advance upgrades in ad algorithm design.
🛠️ Required Skills
- Expertise in large model development and optimization
- Knowledge of model scaling up and scaling laws
- Experience with multimodal technologies in recommendations
- Proficiency in handling large-scale data (billions of samples and features)
- Familiarity with techniques like Embedding migration and model distillation
- Ability to follow and apply latest AI research trends
Locations
- Shenzhen, China
Salary
Estimated Salary Rangemedium confidence
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.
Skills Required
- Expertise in large model development and optimizationintermediate
- Knowledge of model scaling up and scaling lawsintermediate
- Experience with multimodal technologies in recommendationsintermediate
- Proficiency in handling large-scale data (billions of samples and features)intermediate
- Familiarity with techniques like Embedding migration and model distillationintermediate
- Ability to follow and apply latest AI research trendsintermediate
Responsibilities
- Responsible for foundational recommendation algorithm directions such as base large models, model scaling up, multimodal recommendations, etc., using billions of samples and features, combined with model scaling up and multimodal technologies to continuously promote modeling technology upgrades and breakthroughs.
- Base large model modeling and optimization, building base large models based on billions of samples and features, and developing Embedding migration frameworks and model distillation technologies to effectively migrate base large model capabilities to recommendation systems, driving continuous improvement in core metrics like ad CTR/CVR.
- Through scaling up of features, samples, and models, continuously explore the ceiling of model scaling laws.
- Incorporate multimodal technologies into ad recommendation modeling, using richer information and more generalized expressions to continuously improve model performance.
- Actively follow the latest developments in AI academia and industry, optimize internal technical solutions, and continuously advance upgrades in ad algorithm design.
Tags & Categories
Answer 10 quick questions to check your fit for 腾讯广告-大模型推荐算法工程师 @ Tencent.
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腾讯广告-大模型推荐算法工程师
Tencent
腾讯广告-大模型推荐算法工程师
Job Description
腾讯广告-大模型推荐算法工程师
📋 Job Overview
The role focuses on advancing recommendation algorithms for Tencent Advertising using large models, including foundational large models, model scaling up, and multimodal recommendations. Responsibilities include building and optimizing base large models with billions of samples and features, exploring scaling laws, integrating multimodal technologies, and staying updated with AI advancements to enhance ad performance metrics like CTR and CVR. This position drives continuous breakthroughs in modeling techniques to improve advertising effectiveness.
📍 Location: Shenzhen, China
🏢 Business Unit: CDG
📄 Full Description
1.负责广告基础推荐算法方向，如基础大模型、模型 scaling up、多模态推荐等方向，通过千亿样本、特征，结合模型 scaling up和多模态技术，持续推动建模技术升级突破；
2.基础大模型建模和优化，基于千亿样本、特征，构建基座大模型，并研发Embedding迁移框架及模型蒸馏等技术，实现基座大模型能力向推荐系统的有效迁移，驱动广告CTR/CVR等核心指标持续提升；
3.通过特征、样本、模型的scaling up，持续探索模型scaling law 的天花板；
4.将多模态技术融入广告推荐建模中，通过更丰富信息、更泛化的表达，持续提升模型效果；
5.积极跟进AI学术界和业界的最新动态，优化内部技术方案，不断推进广告算法设计升级。
2.基础大模型建模和优化，基于千亿样本、特征，构建基座大模型，并研发Embedding迁移框架及模型蒸馏等技术，实现基座大模型能力向推荐系统的有效迁移，驱动广告CTR/CVR等核心指标持续提升；
3.通过特征、样本、模型的scaling up，持续探索模型scaling law 的天花板；
4.将多模态技术融入广告推荐建模中，通过更丰富信息、更泛化的表达，持续提升模型效果；
5.积极跟进AI学术界和业界的最新动态，优化内部技术方案，不断推进广告算法设计升级。
🎯 Key Responsibilities
- Responsible for foundational recommendation algorithm directions such as base large models, model scaling up, multimodal recommendations, etc., using billions of samples and features, combined with model scaling up and multimodal technologies to continuously promote modeling technology upgrades and breakthroughs.
- Base large model modeling and optimization, building base large models based on billions of samples and features, and developing Embedding migration frameworks and model distillation technologies to effectively migrate base large model capabilities to recommendation systems, driving continuous improvement in core metrics like ad CTR/CVR.
- Through scaling up of features, samples, and models, continuously explore the ceiling of model scaling laws.
- Incorporate multimodal technologies into ad recommendation modeling, using richer information and more generalized expressions to continuously improve model performance.
- Actively follow the latest developments in AI academia and industry, optimize internal technical solutions, and continuously advance upgrades in ad algorithm design.
🛠️ Required Skills
- Expertise in large model development and optimization
- Knowledge of model scaling up and scaling laws
- Experience with multimodal technologies in recommendations
- Proficiency in handling large-scale data (billions of samples and features)
- Familiarity with techniques like Embedding migration and model distillation
- Ability to follow and apply latest AI research trends
Locations
- Shenzhen, China
Salary
Estimated Salary Rangemedium confidence
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.
Skills Required
- Expertise in large model development and optimizationintermediate
- Knowledge of model scaling up and scaling lawsintermediate
- Experience with multimodal technologies in recommendationsintermediate
- Proficiency in handling large-scale data (billions of samples and features)intermediate
- Familiarity with techniques like Embedding migration and model distillationintermediate
- Ability to follow and apply latest AI research trendsintermediate
Responsibilities
- Responsible for foundational recommendation algorithm directions such as base large models, model scaling up, multimodal recommendations, etc., using billions of samples and features, combined with model scaling up and multimodal technologies to continuously promote modeling technology upgrades and breakthroughs.
- Base large model modeling and optimization, building base large models based on billions of samples and features, and developing Embedding migration frameworks and model distillation technologies to effectively migrate base large model capabilities to recommendation systems, driving continuous improvement in core metrics like ad CTR/CVR.
- Through scaling up of features, samples, and models, continuously explore the ceiling of model scaling laws.
- Incorporate multimodal technologies into ad recommendation modeling, using richer information and more generalized expressions to continuously improve model performance.
- Actively follow the latest developments in AI academia and industry, optimize internal technical solutions, and continuously advance upgrades in ad algorithm design.
Tags & Categories
Answer 10 quick questions to check your fit for 腾讯广告-大模型推荐算法工程师 @ Tencent.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs