Amazon

Sells products online and rents cloud computing

Applied Scientist - International Technology Team

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeBeijing, ChinaNo salary listedIn-person

About the Job

Requirements

Currently pursuing or have earned a master's or doctoral degree in computer science, computer engineering, machine learning, artificial intelligence, operations research, statistics, or a related field.
Have foundational knowledge of machine learning or deep learning, and experience with experimental design, statistical analysis, and model evaluation.
Have experience implementing, training, and evaluating algorithms using code and tools.
Be proficient in at least one programming language, such as Python, Java, or C++.
Understand basic methods in at least one of natural language processing, information retrieval, recommendation systems, or generative artificial intelligence.
Be a Chinese citizen.

Responsibilities

Propose verifiable scientific hypotheses and design and implement machine-learning, natural-language-processing, information-retrieval, or large-language-model solutions to real problems in Amazon search and shopping experiences.
Train, fine-tune, and evaluate models using large-scale product, query, and customer-behavior data, and establish reproducible experimentation and evaluation processes.
Explore foundation-model applications in search, recommendations, and conversational shopping, including retrieval-augmented generation, model fine-tuning, prompt optimization, and agents.
Design evaluation metrics covering relevance, factuality, robustness, safety, latency, and cost, and validate outcomes through offline experiments, A/B testing, and customer feedback.
Collaborate with engineering and product teams to turn prototypes into scalable, maintainable production systems, and continuously analyze and improve online performance.
Track developments in academia and industry, produce technical documentation, and share research with internal or external scientific communities where appropriate.

Desired Qualifications

Have published in top conferences or journals in natural language processing, information retrieval, machine learning, data mining, or generative artificial intelligence, or have completed high-quality research projects.
Be familiar with Transformers, large language models, or foundation models, and have practical experience in one or more of pretraining, supervised fine-tuning, parameter-efficient fine-tuning, preference optimization, or inference optimization.
Have research or development experience with retrieval-augmented generation, vector retrieval, embeddings, semantic matching, agents, or tool-calling systems.
Be familiar with deep-learning frameworks such as PyTorch or TensorFlow and common large-language-model toolchains such as Hugging Face Transformers.
Have search-engine or recommendation-system experience, especially with indexing, retrieval, ranking, query understanding, personalization, or online experimentation.
Have experience evaluating large language models and measuring system quality across relevance, factuality, hallucinations, robustness, safety, latency, and cost.
Have experience in large-scale data processing, distributed training, model compression, or efficient inference.
Have critical-thinking and technical-communication skills to clearly explain model choices, experimental results, and limitations, and collaborate with cross-functional teams on open-ended problems.
Have Chinese and English language skills.

Description

北京职位 - 如果希望在北京工作，请投递本职位。

毕业时间：2026年10月 - 2027年9月之间毕业的应届毕业生
· 投递须知：
1 填写简历申请时，请把必填和非必填项都填写完整。提交简历之后就无法修改了哦！
2 学校的英文全称请准确填写。中英文对应表，请点击链接查看 https://docs.qq.com/sheet/DVmdaa1BCV0RBbnlR?tab=BB08J2
3 简历不限中英文。

如果您正在攻读自然语言处理（NLP）、信息检索（IR）、机器学习、生成式人工智能或相关方向的硕士或博士学位，并希望将前沿科学研究转化为服务真实客户的产品，我们诚挚邀请您加入亚马逊 International Technology 搜索团队。
我们的目标是帮助亚马逊客户更准确地找到所需商品，并发现符合其需求和兴趣的新商品。您每天的工作都将直接影响全球数百万客户的购物体验。团队使用 TB 级商品、查询和客户行为数据，持续推进搜索、推荐、自然语言理解以及生成式 AI 技术的发展。
在这个岗位中，您将研究并应用 NLP、IR、深度学习、大语言模型（LLM）和基础模型等前沿技术，解决搜索理解、相关性排序、语义匹配、个性化和对话式购物等问题。您将有机会探索预训练、监督微调（SFT）、参数高效微调、检索增强生成（RAG）、提示优化和智能体（Agent）等技术，并针对业务场景建立可靠的离线与在线评估方法。
您将与应用科学家、软件工程师和产品经理密切合作，完成从问题定义、数据分析、算法设计和实验验证，到模型部署、在线测试和持续迭代的完整闭环。您需要根据客户价值和业务目标选择合适的技术方案，并在模型质量、可靠性、安全性、推理延迟和计算成本之间做出合理权衡。

Key job responsibilities
Key job responsibilities
· 针对 Amazon 搜索和购物体验中的实际问题，提出可验证的科学假设，设计并实现机器学习、NLP、IR 或 LLM 解决方案。
· 使用大规模商品、查询和客户行为数据训练、微调和评估模型，建立可重复的实验与评估流程。
· 探索基础模型在搜索、推荐和对话式购物中的应用，包括 RAG、模型微调、提示优化和 Agent 等方向。
· 设计覆盖相关性、事实性、鲁棒性、安全性、延迟和成本的评估指标，并通过离线实验、A/B 测试和客户反馈验证效果。
· 与工程和产品团队合作，将原型转化为可扩展、可维护的生产系统，并持续分析和改进线上表现。
· 跟踪学术界和工业界的最新进展，形成技术文档，并在适当情况下向内部或外部科学社区分享研究成果。

基本要求
· 正在攻读或已获得计算机科学、计算机工程、机器学习、人工智能、运筹学、统计学或相关领域的硕士或博士学位。
· 具备机器学习或深度学习的基础知识，以及实验设计、统计分析和模型评估经验。
· 具备使用代码和工具实现、训练和评估算法的经验。
· 至少熟练使用一种编程语言，例如 Python、Java 或 C++。
· 了解 NLP、IR、推荐系统或生成式 AI 中至少一个方向的基本方法。

优先条件
· 在 NLP、IR、机器学习、数据挖掘或生成式 AI 相关顶级会议或期刊发表过论文，或有高质量研究项目经历。
· 熟悉 Transformer、LLM 或基础模型，并具有预训练、监督微调（SFT）、参数高效微调、偏好优化或推理优化中的一种或多种实践经验。
· 具有 RAG、向量检索、Embedding、语义匹配、Agent 或工具调用系统的研究或开发经验。
· 熟悉 PyTorch、TensorFlow 等深度学习框架，以及 Hugging Face Transformers 等常用 LLM 工具链。
· 具有搜索引擎或推荐系统经验，尤其是在索引、召回、排序、查询理解、个性化或在线实验方面。
· 具有 LLM 评估经验，能够从相关性、事实性、幻觉、鲁棒性、安全性、延迟和成本等维度衡量系统质量。
· 具有大规模数据处理、分布式训练、模型压缩或高效推理经验。
· 具备良好的批判性思维和技术沟通能力，能够清楚地解释模型选择、实验结果及其局限性，并与跨职能团队合作解决开放性问题。

Basic Qualifications

- Chinese citizen

Preferred Qualifications

- Chinese and English skill

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

About the Company

Amazon

Sells products online and rents cloud computing

Amazon runs the largest online store in the United States and rents cloud computing to businesses and governments through Amazon Web Services. AWS pioneered renting computing power over the internet, and Amazon built its own delivery network to get orders to customers quickly. Shoppers buy from Amazon and from independent sellers on its store, and Prime members pay a subscription for fast free shipping, streaming video and music, and other perks. AWS rents out computing power, storage, databases, and AI tools so companies can run software without owning servers, and Amazon also makes Kindle e-readers and Echo speakers with the Alexa assistant. Jeff Bezos founded Amazon in 1994, and it is based in Seattle, Washington.

Company size: 10,001+
Company stage: IPO
Headquarters: Seattle, WA
Founded: 1994

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