腾讯广告-大模型推荐算法负责人
Tencent
腾讯广告-大模型推荐算法负责人
Job Description
腾讯广告-大模型推荐算法负责人
📋 Job Overview
The role of Head of Large Model Recommendation Algorithm at Tencent Advertising involves leading the development and optimization of foundational recommendation algorithms using large models, scaling techniques, and multimodal technologies. Responsibilities include building base large models with billion-scale samples and features, exploring model scaling laws, and integrating multimodal approaches to enhance ad recommendation performance. The position requires staying abreast of AI advancements to drive continuous improvements in core metrics like CTR and CVR.
📍 Location: Shenzhen, China
🏢 Business Unit: CDG
📄 Full Description
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
- Lead the direction of foundational ad recommendation algorithms, including base large models, model scaling up, and multimodal recommendation, using billion-scale samples and features to drive modeling breakthroughs.
- Build and optimize base large models based on billion-scale samples and features, develop embedding migration frameworks and model distillation techniques to transfer capabilities to recommendation systems, and improve core metrics like CTR/CVR.
- Explore the ceiling of model scaling laws through scaling up of features, samples, and models.
- Incorporate multimodal technologies into ad recommendation modeling to leverage richer information and more generalized representations for better model performance.
- Actively follow the latest AI developments in academia and industry, optimize internal technical solutions, and advance ad algorithm design upgrades.
🛠️ Required Skills
- Expertise in large model building and optimization
- Knowledge of model scaling up and scaling laws
- Proficiency in multimodal technologies for recommendation systems
- Experience with embedding migration and model distillation
- Ability to handle billion-scale samples and features
- Staying updated with AI academic and industry trends
Locations
- Shenzhen, China
Salary
800,000 - 2,000,000 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 building and optimizationintermediate
- Knowledge of model scaling up and scaling lawsintermediate
- Proficiency in multimodal technologies for recommendation systemsintermediate
- Experience with embedding migration and model distillationintermediate
- Ability to handle billion-scale samples and featuresintermediate
- Staying updated with AI academic and industry trendsintermediate
Responsibilities
- Lead the direction of foundational ad recommendation algorithms, including base large models, model scaling up, and multimodal recommendation, using billion-scale samples and features to drive modeling breakthroughs.
- Build and optimize base large models based on billion-scale samples and features, develop embedding migration frameworks and model distillation techniques to transfer capabilities to recommendation systems, and improve core metrics like CTR/CVR.
- Explore the ceiling of model scaling laws through scaling up of features, samples, and models.
- Incorporate multimodal technologies into ad recommendation modeling to leverage richer information and more generalized representations for better model performance.
- Actively follow the latest AI developments in academia and industry, optimize internal technical solutions, and advance ad algorithm design upgrades.
Tags & Categories
Answer 10 quick questions to check your fit for 腾讯广告-大模型推荐算法负责人 @ Tencent.
Related Books and Jobs
腾讯广告-大模型推荐算法负责人
Tencent
腾讯广告-大模型推荐算法负责人
Job Description
腾讯广告-大模型推荐算法负责人
📋 Job Overview
The role of Head of Large Model Recommendation Algorithm at Tencent Advertising involves leading the development and optimization of foundational recommendation algorithms using large models, scaling techniques, and multimodal technologies. Responsibilities include building base large models with billion-scale samples and features, exploring model scaling laws, and integrating multimodal approaches to enhance ad recommendation performance. The position requires staying abreast of AI advancements to drive continuous improvements in core metrics like CTR and CVR.
📍 Location: Shenzhen, China
🏢 Business Unit: CDG
📄 Full Description
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
- Lead the direction of foundational ad recommendation algorithms, including base large models, model scaling up, and multimodal recommendation, using billion-scale samples and features to drive modeling breakthroughs.
- Build and optimize base large models based on billion-scale samples and features, develop embedding migration frameworks and model distillation techniques to transfer capabilities to recommendation systems, and improve core metrics like CTR/CVR.
- Explore the ceiling of model scaling laws through scaling up of features, samples, and models.
- Incorporate multimodal technologies into ad recommendation modeling to leverage richer information and more generalized representations for better model performance.
- Actively follow the latest AI developments in academia and industry, optimize internal technical solutions, and advance ad algorithm design upgrades.
🛠️ Required Skills
- Expertise in large model building and optimization
- Knowledge of model scaling up and scaling laws
- Proficiency in multimodal technologies for recommendation systems
- Experience with embedding migration and model distillation
- Ability to handle billion-scale samples and features
- Staying updated with AI academic and industry trends
Locations
- Shenzhen, China
Salary
800,000 - 2,000,000 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 building and optimizationintermediate
- Knowledge of model scaling up and scaling lawsintermediate
- Proficiency in multimodal technologies for recommendation systemsintermediate
- Experience with embedding migration and model distillationintermediate
- Ability to handle billion-scale samples and featuresintermediate
- Staying updated with AI academic and industry trendsintermediate
Responsibilities
- Lead the direction of foundational ad recommendation algorithms, including base large models, model scaling up, and multimodal recommendation, using billion-scale samples and features to drive modeling breakthroughs.
- Build and optimize base large models based on billion-scale samples and features, develop embedding migration frameworks and model distillation techniques to transfer capabilities to recommendation systems, and improve core metrics like CTR/CVR.
- Explore the ceiling of model scaling laws through scaling up of features, samples, and models.
- Incorporate multimodal technologies into ad recommendation modeling to leverage richer information and more generalized representations for better model performance.
- Actively follow the latest AI developments in academia and industry, optimize internal technical solutions, and advance ad algorithm design upgrades.
Tags & Categories
Answer 10 quick questions to check your fit for 腾讯广告-大模型推荐算法负责人 @ Tencent.
Related Books and Jobs