{
    "schema_version": "via_ai_product_manager_jobs_v1",
    "data_type": "ai_product_manager_jobs",
    "job": {
        "job_title": "面向国际电商场景的大模型应用-国际电商（新加坡）LLM Applications for International E-commerce Scenarios-Global E-Commerce (Singapore)",
        "job_level": "校招",AI生成
        "work_category": "全职",AI生成
        "department": "未披露",
        "location": "新加坡",
        "work_mode": "未披露",AI生成
        "job_category": "算法"AI生成
    },
    "company": {
        "company_name": "字节跳动",
        "company_industry": "互联网 / 数字内容 / 电商",
        "company_size": "10000人以上",
        "company_website": "bytedance.com",
        "company_description": "字节跳动提供内容创作、信息分发、电商和协作产品，旗下产品包括抖音、今日头条、TikTok和飞书。"
    },
    "compensation": {
        "salary_range": {
            "min": "",
            "max": "",
            "currency": "未披露",
            "cycle": "未披露"
        },
        "benefits": "未披露"
    },
    "requirements": {
        "working_years": "",
        "degree": "获得博士学位",
        "language": "未披露",AI生成
        "visa_support": "未披露",
        "responsibilities": "团队介绍：国际电商是以TikTok为载体的电商业务（也称为TikTok Shop），致力于成为用户发现并获取优价好物的首选平台，在直播电商、视频内容电商、货架电商等多场景下，国际电商希望能为用户提供更个性化、更主动、更高效的消费体验，为商家提供稳定可靠的平台服务，致力于新奇好物畅销全球，美好生活触手可得的使命。\nData-电商团队是国际电商的核心算法技术力量，专注于电商领域的算法创新，帮助用户高效发现感兴趣的商品，保障用户的购物安全，提升交易各环节的智能化水平。在这里，你将与一流的产品和技术团队合作、钻研，一起应对技术和业务上的挑战，推动技术在电商场景的深度落地。\n\n课题介绍：\n国际电商生态中沉淀了用户行为、商品图文、多媒体内容、商品销量与物流时序等海量异构数据，但传统模型在长周期预测、跨模态理解及复杂决策推理上仍存在明显瓶颈。\n本课题拟以大模型为基础，联合构建面向国际电商场景的基础大模型，将用户、商品、内容、物流与库存等关键信息统一建模，并在其之上设计可插拔的Agent框架，系统整合任务规划、工具调用、多轮交互与环境感知等能力，从而在需求预测、流量分发与个性化推荐等链路中实现端到端的智能决策。\n\n课题挑战：\n1、异构融合与对齐：统一建模用户行为序列、商品销量时序信号与多模态商品内容，完成高维时序与图文表征的深度语义对齐；\n2、推荐大模型与世界模型协同：把推荐问题定义还原为用户推荐列表的生成问题，基于大模型的技术完成端到端推荐建模；\n3、推荐物品的Tokenizor：如何把亿级别的物品进行多模态和特征语义编码，支撑后续训练和生成任务，处理几十TB级别的用户行为Tokens的预训练，通过模型结构和训练方式拉高Scaling Law曲线，把各类推荐任务重构为后训练任务，以RLVR的思路进行推荐任务建模，最大化GMV和体验价值，训练推理优化，基于SGLang 等大模型推理套件定制构建高性能的推荐服务；\n4、电商多模态大模型：构建面向电商领域的多语言多模态大模型，在核心电商场景达到SOTA性能，并以此为基础打造电商智能体基座，广泛支撑各类电商场景下的Agent应用落地；\n5、Agent评测与安全合规：构建贴合实际业务的Agent评测指标与基准，保障在强约束、强对抗环境下的稳定性、安全性与合规性。\n\n课题价值：\n1、技术价值：打造通用多模态基座，以模型、数据、算力迭代实现幂律增长，夯实规模化技术底座；\n2、业务价值：搭建国际电商大模型底座，以生成式推荐、时序大模型、Agent等驱动GMV与留存，打造高杠杆营收引擎。\n\nTopic Content:\nIn today’s global e-commerce landscape, intelligent systems must operate across increasingly complex and dynamic business environments. Yet existing approaches still face limitations in long-horizon forecasting, cross-modal understanding, and holistic decision-making.This initiative is focused on building a next-generation foundational large model purpose-built for global e-commerce applications. The model will integrate key business dimensions—such as users, products, content, logistics, and inventory—into a unified representation to support deep, context-aware intelligence at scale.Building on this foundation, we are developing a modular, agent-driven architecture that enables advanced capabilities including task planning, tool use, multi-turn reasoning, and real-world environment interaction.Together, these innovations aim to power end-to-end intelligent decision-making across critical e-commerce scenarios, including demand forecasting, traffic optimization, and personalized recommendation systems.\n\nTopic Challenges:\n1.Heterogeneous fusion and alignment;\n2.Synergy between recommendation LLMs and world models;\n3.Tokenizer of recommendation items;\n4.Multimodal large models for e-commerce;\n5.Agent evaluation, safety, and compliance.\n\nTopic Value:\n1.Technical value: Building a general-purpose multimodal foundation to enable power-law scaling through iterative advancements in models, data, and compute, thereby strengthening the infrastructure for scalable AI foundations;\n2.Business value: Establishing a global e-commerce foundation model to drive GMV growth and user retention through generative recommendation, time-series large models, and agent-based systems, ultimately creating a high-leverage revenue engine.",
        "requirements_text": "1、2027届毕业，获得博士学位，计算机科学、人工智能、数学或相关专业；\n2、人工智能/机器学习专业能力：对深度学习、自然语言处理、计算机视觉、强化学习、生成式模型或多模态学习领域有深入理解及相关研究经验；\n3、编程与工程能力：熟练掌握主流编程语言及机器学习框架（如PyTorch、TensorFlow），同时具备出色的问题解决能力、自主学习能力与团队协作能力。\n\n加分项\n1、学术能力：在国际人工智能/计算机领域顶级会议或期刊（如NeurIPS、ICML、ICLR、CVPR、ACL、KDD、SIGIR、WWW）发表过论文，或在权威算法竞赛中取得优异名次；\n2、领域实践成果：主导或参与过搜索、广告、推荐系统或大语言模型（LLMs）相关核心项目，具备实操经验；\n3、高级多模态应用能力：在多模态大模型领域具备专项技术专长，尤其擅长长文本处理，或拥有影视剧集领域相关应用落地经验。\n\n1.Education & Foundation: Ph.D. in Computer Science, AI, Mathematics, or a related field, with a strong foundation in data structures, algorithms, and mathematical modeling;\n2.AI/ML Expertise: Solid understanding and research experience in Deep Learning, NLP, CV, Reinforcement Learning, Generative Models, or Multimodal Learning;\n3.Coding & Engineering: Proficient in major programming languages and machine learning frameworks (e.g., PyTorch, TensorFlow), combined with excellent problem-solving, self-learning, and teamwork skills.\n\nPreferred Qualifications\n1.Academic Excellence: Proven track record of publications in international AI/CS conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, KDD, SIGIR, WWW) or top rankings in recognized algorithmic competitions;\n2.Domain-Specific Impact: Hands-on experience in leading or participating in key projects related to Search, Advertising, Recommendation systems, or Large Language Models (LLMs);\n3.Advanced Multimodal Applications: Specialized expertise in multimodal large models, particularly in long-text processing or applications within the film and television drama domains.",
        "skills": [AI生成
            "PyTorch",
            "TensorFlow",
            "机器学习",
            "深度学习",
            "强化学习",
            "自然语言处理",
            "计算机视觉",
            "团队协作",
            "学习能力",
            "大语言模型"
        ],
        "preferred_qualifications": "加分项；Preferred Qualifications",
        "job_highlights": "未披露"
    },
    "contact": {
        "recruiter_name": "未披露",
        "contact_methods": [
            {
                "type": "apply_link",
                "value": "https://jobs.bytedance.com/campus/position/7630869633486358789/detail"
            }
        ]
    },
    "posting": {
        "posting_id": "7630869633486358789",
        "post_date": "2026-04-20",
        "hire_time": "未披露",
        "update_date": "20260917",
        "job_url": "https://jobs.bytedance.com/campus/position/7630869633486358789/detail"
    },
    "fulltext": "面向国际电商场景的大模型应用-国际电商（新加坡）LLM Applications for International E-commerce Scenarios-Global E-Commerce (Singapore)\n职位描述\n团队介绍：国际电商是以TikTok为载体的电商业务（也称为TikTok Shop），致力于成为用户发现并获取优价好物的首选平台，在直播电商、视频内容电商、货架电商等多场景下，国际电商希望能为用户提供更个性化、更主动、更高效的消费体验，为商家提供稳定可靠的平台服务，致力于新奇好物畅销全球，美好生活触手可得的使命。\nData-电商团队是国际电商的核心算法技术力量，专注于电商领域的算法创新，帮助用户高效发现感兴趣的商品，保障用户的购物安全，提升交易各环节的智能化水平。在这里，你将与一流的产品和技术团队合作、钻研，一起应对技术和业务上的挑战，推动技术在电商场景的深度落地。\n\n课题介绍：\n国际电商生态中沉淀了用户行为、商品图文、多媒体内容、商品销量与物流时序等海量异构数据，但传统模型在长周期预测、跨模态理解及复杂决策推理上仍存在明显瓶颈。\n本课题拟以大模型为基础，联合构建面向国际电商场景的基础大模型，将用户、商品、内容、物流与库存等关键信息统一建模，并在其之上设计可插拔的Agent框架，系统整合任务规划、工具调用、多轮交互与环境感知等能力，从而在需求预测、流量分发与个性化推荐等链路中实现端到端的智能决策。\n\n课题挑战：\n1、异构融合与对齐：统一建模用户行为序列、商品销量时序信号与多模态商品内容，完成高维时序与图文表征的深度语义对齐；\n2、推荐大模型与世界模型协同：把推荐问题定义还原为用户推荐列表的生成问题，基于大模型的技术完成端到端推荐建模；\n3、推荐物品的Tokenizor：如何把亿级别的物品进行多模态和特征语义编码，支撑后续训练和生成任务，处理几十TB级别的用户行为Tokens的预训练，通过模型结构和训练方式拉高Scaling Law曲线，把各类推荐任务重构为后训练任务，以RLVR的思路进行推荐任务建模，最大化GMV和体验价值，训练推理优化，基于SGLang 等大模型推理套件定制构建高性能的推荐服务；\n4、电商多模态大模型：构建面向电商领域的多语言多模态大模型，在核心电商场景达到SOTA性能，并以此为基础打造电商智能体基座，广泛支撑各类电商场景下的Agent应用落地；\n5、Agent评测与安全合规：构建贴合实际业务的Agent评测指标与基准，保障在强约束、强对抗环境下的稳定性、安全性与合规性。\n\n课题价值：\n1、技术价值：打造通用多模态基座，以模型、数据、算力迭代实现幂律增长，夯实规模化技术底座；\n2、业务价值：搭建国际电商大模型底座，以生成式推荐、时序大模型、Agent等驱动GMV与留存，打造高杠杆营收引擎。\n\nTopic Content:\nIn today’s global e-commerce landscape, intelligent systems must operate across increasingly complex and dynamic business environments. Yet existing approaches still face limitations in long-horizon forecasting, cross-modal understanding, and holistic decision-making.This initiative is focused on building a next-generation foundational large model purpose-built for global e-commerce applications. The model will integrate key business dimensions—such as users, products, content, logistics, and inventory—into a unified representation to support deep, context-aware intelligence at scale.Building on this foundation, we are developing a modular, agent-driven architecture that enables advanced capabilities including task planning, tool use, multi-turn reasoning, and real-world environment interaction.Together, these innovations aim to power end-to-end intelligent decision-making across critical e-commerce scenarios, including demand forecasting, traffic optimization, and personalized recommendation systems.\n\nTopic Challenges:\n1.Heterogeneous fusion and alignment;\n2.Synergy between recommendation LLMs and world models;\n3.Tokenizer of recommendation items;\n4.Multimodal large models for e-commerce;\n5.Agent evaluation, safety, and compliance.\n\nTopic Value:\n1.Technical value: Building a general-purpose multimodal foundation to enable power-law scaling through iterative advancements in models, data, and compute, thereby strengthening the infrastructure for scalable AI foundations;\n2.Business value: Establishing a global e-commerce foundation model to drive GMV growth and user retention through generative recommendation, time-series large models, and agent-based systems, ultimately creating a high-leverage revenue engine.\n\n职位要求\n1、2027届毕业，获得博士学位，计算机科学、人工智能、数学或相关专业；\n2、人工智能/机器学习专业能力：对深度学习、自然语言处理、计算机视觉、强化学习、生成式模型或多模态学习领域有深入理解及相关研究经验；\n3、编程与工程能力：熟练掌握主流编程语言及机器学习框架（如PyTorch、TensorFlow），同时具备出色的问题解决能力、自主学习能力与团队协作能力。\n\n加分项\n1、学术能力：在国际人工智能/计算机领域顶级会议或期刊（如NeurIPS、ICML、ICLR、CVPR、ACL、KDD、SIGIR、WWW）发表过论文，或在权威算法竞赛中取得优异名次；\n2、领域实践成果：主导或参与过搜索、广告、推荐系统或大语言模型（LLMs）相关核心项目，具备实操经验；\n3、高级多模态应用能力：在多模态大模型领域具备专项技术专长，尤其擅长长文本处理，或拥有影视剧集领域相关应用落地经验。\n\n1.Education & Foundation: Ph.D. in Computer Science, AI, Mathematics, or a related field, with a strong foundation in data structures, algorithms, and mathematical modeling;\n2.AI/ML Expertise: Solid understanding and research experience in Deep Learning, NLP, CV, Reinforcement Learning, Generative Models, or Multimodal Learning;\n3.Coding & Engineering: Proficient in major programming languages and machine learning frameworks (e.g., PyTorch, TensorFlow), combined with excellent problem-solving, self-learning, and teamwork skills.\n\nPreferred Qualifications\n1.Academic Excellence: Proven track record of publications in international AI/CS conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, KDD, SIGIR, WWW) or top rankings in recognized algorithmic competitions;\n2.Domain-Specific Impact: Hands-on experience in leading or participating in key projects related to Search, Advertising, Recommendation systems, or Large Language Models (LLMs);\n3.Advanced Multimodal Applications: Specialized expertise in multimodal large models, particularly in long-text processing or applications within the film and television drama domains.\n团队介绍：国际电商是以TikTok为载体的电商业务（也称为TikTok Shop），致力于成为用户发现并获取优价好物的首选平台，在直播电商、视频内容电商、货架电商等多场景下，国际电商希望能为用户提供更个性化、更主动、更高效的消费体验，为商家提供稳定可靠的平台服务，致力于新奇好物畅销全球，美好生活触手可得的使命。\nData-电商团队是国际电商的核心算法技术力量，专注于电商领域的算法创新，帮助用户高效发现感兴趣的商品，保障用户的购物安全，提升交易各环节的智能化水平。在这里，你将与一流的产品和技术团队合作、钻研，一起应对技术和业务上的挑战，推动技术在电商场景的深度落地。\n\n课题介绍：\n国际电商生态中沉淀了用户行为、商品图文、多媒体内容、商品销量与物流时序等海量异构数据，但传统模型在长周期预测、跨模态理解及复杂决策推理上仍存在明显瓶颈。\n本课题拟以大模型为基础，联合构建面向国际电商场景的基础大模型，将用户、商品、内容、物流与库存等关键信息统一建模，并在其之上设计可插拔的Agent框架，系统整合任务规划、工具调用、多轮交互与环境感知等能力，从而在需求预测、流量分发与个性化推荐等链路中实现端到端的智能决策。\n\n课题挑战：\n1、异构融合与对齐：统一建模用户行为序列、商品销量时序信号与多模态商品内容，完成高维时序与图文表征的深度语义对齐；\n2、推荐大模型与世界模型协同：把推荐问题定义还原为用户推荐列表的生成问题，基于大模型的技术完成端到端推荐建模；\n3、推荐物品的Tokenizor：如何把亿级别的物品进行多模态和特征语义编码，支撑后续训练和生成任务，处理几十TB级别的用户行为Tokens的预训练，通过模型结构和训练方式拉高Scaling Law曲线，把各类推荐任务重构为后训练任务，以RLVR的思路进行推荐任务建模，最大化GMV和体验价值，训练推理优化，基于SGLang 等大模型推理套件定制构建高性能的推荐服务；\n4、电商多模态大模型：构建面向电商领域的多语言多模态大模型，在核心电商场景达到SOTA性能，并以此为基础打造电商智能体基座，广泛支撑各类电商场景下的Agent应用落地；\n5、Agent评测与安全合规：构建贴合实际业务的Agent评测指标与基准，保障在强约束、强对抗环境下的稳定性、安全性与合规性。\n\n课题价值：\n1、技术价值：打造通用多模态基座，以模型、数据、算力迭代实现幂律增长，夯实规模化技术底座；\n2、业务价值：搭建国际电商大模型底座，以生成式推荐、时序大模型、Agent等驱动GMV与留存，打造高杠杆营收引擎。\n\nTopic Content:\nIn today’s global e-commerce landscape, intelligent systems must operate across increasingly complex and dynamic business environments. Yet existing approaches still face limitations in long-horizon forecasting, cross-modal understanding, and holistic decision-making.This initiative is focused on building a next-generation foundational large model purpose-built for global e-commerce applications. The model will integrate key business dimensions—such as users, products, content, logistics, and inventory—into a unified representation to support deep, context-aware intelligence at scale.Building on this foundation, we are developing a modular, agent-driven architecture that enables advanced capabilities including task planning, tool use, multi-turn reasoning, and real-world environment interaction.Together, these innovations aim to power end-to-end intelligent decision-making across critical e-commerce scenarios, including demand forecasting, traffic optimization, and personalized recommendation systems.\n\nTopic Challenges:\n1.Heterogeneous fusion and alignment;\n2.Synergy between recommendation LLMs and world models;\n3.Tokenizer of recommendation items;\n4.Multimodal large models for e-commerce;\n5.Agent evaluation, safety, and compliance.\n\nTopic Value:\n1.Technical value: Building a general-purpose multimodal foundation to enable power-law scaling through iterative advancements in models, data, and compute, thereby strengthening the infrastructure for scalable AI foundations;\n2.Business value: Establishing a global e-commerce foundation model to drive GMV growth and user retention through generative recommendation, time-series large models, and agent-based systems, ultimately creating a high-leverage revenue engine.\n1、2027届毕业，获得博士学位，计算机科学、人工智能、数学或相关专业；\n2、人工智能/机器学习专业能力：对深度学习、自然语言处理、计算机视觉、强化学习、生成式模型或多模态学习领域有深入理解及相关研究经验；\n3、编程与工程能力：熟练掌握主流编程语言及机器学习框架（如PyTorch、TensorFlow），同时具备出色的问题解决能力、自主学习能力与团队协作能力。\n\n加分项\n1、学术能力：在国际人工智能/计算机领域顶级会议或期刊（如NeurIPS、ICML、ICLR、CVPR、ACL、KDD、SIGIR、WWW）发表过论文，或在权威算法竞赛中取得优异名次；\n2、领域实践成果：主导或参与过搜索、广告、推荐系统或大语言模型（LLMs）相关核心项目，具备实操经验；\n3、高级多模态应用能力：在多模态大模型领域具备专项技术专长，尤其擅长长文本处理，或拥有影视剧集领域相关应用落地经验。\n\n1.Education & Foundation: Ph.D. in Computer Science, AI, Mathematics, or a related field, with a strong foundation in data structures, algorithms, and mathematical modeling;\n2.AI/ML Expertise: Solid understanding and research experience in Deep Learning, NLP, CV, Reinforcement Learning, Generative Models, or Multimodal Learning;\n3.Coding & Engineering: Proficient in major programming languages and machine learning frameworks (e.g., PyTorch, TensorFlow), combined with excellent problem-solving, self-learning, and teamwork skills.\n\nPreferred Qualifications\n1.Academic Excellence: Proven track record of publications in international AI/CS conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, KDD, SIGIR, WWW) or top rankings in recognized algorithmic competitions;\n2.Domain-Specific Impact: Hands-on experience in leading or participating in key projects related to Search, Advertising, Recommendation systems, or Large Language Models (LLMs);\n3.Advanced Multimodal Applications: Specialized expertise in multimodal large models, particularly in long-text processing or applications within the film and television drama domains.",
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