Careers

We are seeking an Applied Science Manager to lead a new business unit on the GameLift team focused on creating AI and ML-based applications for the gaming industry. This leader will own the technical vision, scientific rigor, and end-to-end delivery of applied science initiatives that solve complex problems in machine learning, data science, and live-service gaming at scale. The ideal candidate operates at the frontier of AI research and deployment, translating ambiguous business opportunities into production-grade ML systems that generate measurable customer and commercial impact. This role demands a hands-on technical leader who can define and execute an applied science roadmap while managing and mentoring a high-performing team of builders, scientists and ML engineers. You will be responsible for upholding the highest standards of scientific excellence, including rigorous experimental design, disciplined model selection, and reproducible evaluation methodology, while maintaining the speed and inventive culture of a startup operating within a large organization. You will partner with engineering, product, and business stakeholders to bring AI-powered products from research through production deployment, meeting customer requirements and delivery timelines. The successful candidate will be equally comfortable debating the merits of deep learning architectures in a technical review as they are presenting a product roadmap to senior leadership, and will thrive in an environment where building something new from zero to one is the daily expectation. Key job responsibilities Define and execute the technical roadmap for applied science initiatives, balancing frontier research with production delivery requirements and customer timelines Lead rigorous model development processes including algorithm selection, offline evaluation, A/B testing design, and statistical significance assessment, ensuring every production decision is grounded in scientific evidence rather than intuition Architect scalable, reusable ML platforms and inference systems designed to serve multiple products without proportional increases in staffing or rebuild cycles, enabling the team to move fast across a growing portfolio Manage and develop a team of applied scientists and ML engineers, providing technical mentorship, career growth opportunities, and performance management while maintaining a high hiring bar Drive end-to-end AI deployment from research prototyping through production inference, owning latency, availability, and cost targets alongside model quality metrics Establish and enforce scientific standards across all team projects, including peer review mechanisms, documentation requirements, and reproducibility practices that ensure technical decisions withstand scrutiny Partner cross-functionally with product managers, software engineers, and business leaders to translate customer problems into well-scoped technical solutions with clear success criteria Maintain a builder roadmap that sequences product launches against customer commitments, managing dependencies and communicating tradeoffs to stakeholders when scope or timeline pressure arises Enable the team to operate with startup-level autonomy and speed of invention while maintaining the operational discipline required for production systems serving customers at scale Stay current with developments across the AI/ML research landscape, identifying opportunities to apply new techniques A day in the life Your morning starts with production system health checks: inference latency, model freshness, experiment dashboards. Mid-morning you lead a technical design review, challenging your scientists on model complexity tradeoffs and coaching toward disciplined, phased approaches that maintain rigor without sacrificing speed. After lunch you join a cross-functional sync with product and engineering to align on delivery milestones, working through scope tradeoffs when customer requirements shift. Late afternoon is for people leadership: one-on-ones focused on career growth, reviewing hiring scorecards to keep the bar high, and scanning recent research for techniques your team can apply next quarter. Every day blends science, product delivery, and team building.

Fulfillment by Amazon (FBA) is a service that enables sellers to outsource supply chain and fulfillment to Amazon and use Amazon's world-class science, technology, and logistics infrastructure to deliver billions of products from manufacturing hubs to customer doorsteps worldwide with fast delivery promise. The FBA organization is looking for a Principal Economist with expertise in economic and econometric modelling and demonstrated strength in market mechanism design to join our cross-domain group of economists, data scientists, applied and research scientists and scholars. As a lead economist, you will design markets and implement agentic systems that deploy supply chain and fulfillment resources to millions of heterogenous sellers. You will build causal inference models and experiments to evaluate policy impact on seller outcomes, and shape how our products evolve into trustworthy autonomous systems — collaborating with business and software teams to solve key challenges facing the worldwide FBA business. Such challenges include designing mechanisms to align sellers' decisions with customers' needs through better coordinating inventory, inbound, capacity, and fee. Successful operations enable sellers' businesses growth, while ensuring worldwide Amazon customers have access to the largest selection of products through FBA sellers. In doing so, you will shape the economics of Amazon's global fast delivery programs, including Sub Same Day Delivery and Quick Commerce, across North America, Europe, and emerging markets. We are looking for a seasoned economist who brings rigorous causal and structural thinking to traditionally operations research problems and who thrives in the ambiguity of defining the roadmap rather than receiving it. The successful candidate will have familiarity with modern GenAI methods for automation and rapid prototyping. Beyond individual contribution, you will set the long-term technical vision across work streams, and influence product managers, engineers, scientists, and senior leaders on high-judgment decisions and trade-off. You will raise the bar for the organization by establishing best practices, driving science culture, and mentoring junior economists and scientists. We value deeply technical people who deliver results incrementally and frequently in a fast-paced, high-energy and fun environment, and who are eager to learn new areas and develop themselves and their colleagues. Key job responsibilities • Design markets (e.g., auctions), incentive mechanisms (e.g. pricing), develop economic models and execute large-scale experiments to increase supply chain efficiency, to evaluate seller-facing policies, to induce proper seller actions, and to uncover new opportunities that improve customers and sellers’ outcomes. • Shape the economics of Amazon's fast delivery programs and FBA sellers’ product selection strategy (e.g., Sub Same Day Delivery and Quick Commerce) • Bridge economics and operations research by building economic frameworks for large-scale supply chain and fulfilment management problems. • Operate as a thought leader across the organization; collaborate with product managers, scientists, and software developers to incorporate models into production processes and • Influence senior leaders at VP-level on technical and business direction, and represent the science perspective. • Identify and propose new science investment areas to business leaders, shaping where the team focuses next. • Mentor and develop junior economists and scientists, and raise the technical bar for the broader science community. About the team Sellers play a vital role in Amazon's ecosystem, integral to our mission of offering the Earth's largest selection, lowest prices, and fastest delivery speed. FBA is an optional service that enables third-party sellers to outsource order fulfillment to Amazon, and leverage Amazon's world-class facilities to provide customers fast delivery promise. With commitment to taking on even more of the supply chain and operational complexities on behalf of our selling partners, Amazon now provides an end-to-end suite of supply chain services. This comprehensive solution empowers sellers to reliably transport products from manufacturing sites to customers worldwide. The FBA team is the core group in charge of warehousing, inventory management, fulfillment and pricing, and a diverse range of recommendation and agentic services for sellers, as well as building the autonomous internal resource management systems. We work to learn seller behavior, understand seller experience, build automated and trustworthy autonomous assistants to sellers, recommend right actions to sellers, design seller policies and incentives, and develop science products and services that empower sellers to grow their businesses. To do so, we build and innovate science solutions that leverage the right tolls across different fields including economics, operation research, machine learning, statistics, and data analytics. Our culture is centered on rapid prototyping, rigorous experimentation, and data-driven decision-making. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, or Sunnyvale, CA.

Amazon Advertising is a fast-growing multi-billion dollar business that spans desktop, mobile, and connected devices; encompasses ads on Amazon and a vast network of hundreds of thousands of third-party publishers; and extends across US, EU, and an expanding number of international geographies. The Trusted Supply organization has the charter to safeguard advertiser trust and ensure high-quality ad impressions across all Amazon Advertising surfaces. We develop advanced algorithms and infrastructure systems to protect advertisers from unsafe content adjacency, low-quality inventory, fraud and privacy threats. Our scope spans a wide variety of problems in computational advertising including brand safety classification, content suitability scoring, risk hunting and proactive threat detection, viewability prediction, Made-for-Advertising (MFA) detection, malvertising identification, and privacy-preserving measurement and integration. We are looking for an exceptional Principal Applied Scientist to define and drive the science vision across Brand Safety, Suitability, and Risk Hunting as primary areas of focus, while contributing to broader Supply Quality challenges around viewability, privacy-preserving solutions, and data leakage prevention. This is a high-visibility leadership role where your models and systems will process billions of ad impressions daily, directly impacting advertiser confidence, customer experience, and a multi-billion dollar business. Key job responsibilities Set the science vision — defining multi-year research directions, establishing the publication roadmap, and driving innovations Operate across programs — influence modeling frameworks across brand safety, MFA detection, traffic quality, viewability, and 3P integrations; break down silos between science and engineering teams Act as a thought leader — anticipate industry shifts (privacy regulations, adversarial evolution, GenAI-powered threats), propose counter-strategies before they become critical, and represent Amazon in industry forums (TAG, MRC, IAB) Hire, mentor, and grow a high-performing team of applied scientists and research engineers; establish a culture of scientific rigor, peer-reviewed publications, and rapid experimentation Partner with engineering leaders to build efficient, scalable, low-latency production systems that serve models at billions-of-requests-per-day scale Influence product and business strategy — translate science capabilities into advertiser-facing products (targeting controls, transparency reports, quality guarantees) and quantify business impact

Are you passionate about solving complex logistics challenges? Our Analytics team is at the forefront of enhancing delivery experiences through data-driven solutions and innovative technology. As a Research Scientist, you will join a team dedicated to optimizing our delivery network, ensuring reliable and efficient service to our customers. We are seeking an enthusiastic, customer-centric professional with strong analytical capabilities to drive impactful projects, implement advanced solutions, and develop scalable processes. In this role, you will have immediate ownership of business-critical challenges and the opportunity to make strategic, data-driven decisions that shape the future of our delivery operations. Your work will directly influence customer experience and operational excellence. The ideal candidate will possess both research science capabilities and program management skills, thriving in an environment that requires independent decision-making and comfort with ambiguity. This role offers the opportunity to make a significant impact on our advanced logistics network while working with pioneering technology and data science applications. Basic qualifications • 3+ years of building machine learning models for business application experience • Knowledge of programming languages such as C/C++, Python, Java or Perl • Experience programming in Java, C++, Python or related language • Experience with neural deep learning methods and machine learning Preferred qualifications: • PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field • 3+ years of extensive relevant research experience • Deep expertise in Machine Learning • Proficiency in programming • Core competency in mathematics and statistics • Track record of successful projects in algorithm design and product development • Publications at peer-reviewed conferences or journals • Strategic thinker with good execution skills • Exhibits excellent business judgment • Effective verbal and written communication skills • Experience working with real-world data sets and building scalable models from big data • Experience with modern modeling tools and frameworks such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow • Experience with large scale distributed systems

As an Applied Scientist on the Science SW team, you will collaborate closely with other scientists and engineers to bring Reinforcement Learning (RL) research to production. This role combines the scientific application of ML, and specifically RL and sequential decision making, with software development engineering and a strong product focus. It will be your job to design, implement, and deploy novel RL agents, reward models, and control policies in both prototype and production environments, and to prove their impact in high-fidelity simulation before scaling them across the fleet. Key job responsibilities • Own the research and development of reinforcement learning and sequential decision making solutions spanning deep RL, policy optimization, offline/batch RL, contextual bandits, and multi-agent RL for real-time MHE control and building-wide optimization in a production environment. • Formulate fulfillment operations problems (throughput optimization, flow, merge, and congestion control) as sequential decision-making problems, and design multi-objective reward functions that balance competing operational objectives. • Build and leverage high-fidelity simulation environments for safe offline training, policy validation, and sim-to-real transfer before fleet-scale deployment. • Collaborate across multiple science and engineering teams to integrate RL policies into real-time production and control systems. About the team Amazon is building next generation software, hardware, and processes that will run our global network of fulfillment centers that move millions of units of inventory, and ensure customers get what they want when promised. The Science Software team in the One MHS organization unlocks Material Handling Equipment (MHE) innovation through a multiplicity of disciplines within Artificial Intelligence (AI) and applied science, including Computer Vision (CV), Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware prototyping. Rooted in first principles aligned experimentation, the team is dedicated to building self-optimizing fulfillment centers, developing the models that drive real-time, building-wide orchestration of MHE. We conduct experiments, develop models, and apply machine learning (ML) at scale to optimize throughput, flow, merge, and congestion control, and to improve operational performance across the fulfillment network.

We are seeking an exceptional Applied Scientist, Seller Abuse Prevention, to lead the development and implementation of advanced AI solutions that will transform how we prevent bad actors from operating in our store and enable Selling Partners to start and grow their business without fear of disruption, so that customers and Selling Partners across the globe trust us and have confidence in the integrity of Amazon's store. This role will focus on building risk detection models leveraging state-of-the-art AI, including small language models, to detect and prevent abuse of Amazon's catalog worldwide. You will design, develop, and deploy scalable AI solutions to proactively detect and prevent marketplace abuse throughout the seller lifecycle. You will work with massive-scale, multi-modal datasets spanning behavioral patterns, transactional histories, and behavioral data to build detection systems that are ahead of evolving adversarial tactics. Key job responsibilities * Design and build predictive risk detection models using advanced AI techniques, including Natural Language Processing including LLMs and agents to proactively identify bad actors and prevent marketplace abuse at scale * Own the end-to-end scientific solution from risk quantification through decision optimization, determining the appropriate actions to take across varying risk levels * Develop interpretability and reasoning pipelines that provide transparent, actionable explanations for model decisions to support enforcement and seller experience * Work with risk programs across the seller lifecycle to define detection strategies, translate operational investigation patterns into automated systems, and prioritize high-impact risk areas * Partner with engineering teams to deploy models into production, define evaluation frameworks, and collaborate with operations and verification teams to measure and improve detection effectiveness A day in the life Day-to-day you can expect to: - Explore datasets to understand predictors and patterns of abuse - Work with product, program, and engineering stakeholders to build solutions into production that will last - Identify new and emerging abuse vectors as abusers get more sophisticated - Use search, graph, computer vision, NLP, and anomaly detection methodologies to automatically detect abusive actions. About the team Seller Abuse Prevention detects abuse across 4 distinct spaces of abuse: catalog, review, financial risk, and discovery/competitor abuse. Seller Abuse Prevention is embedded in a team of scientists that tackle cross-spanning risk prevention problems. The team has expertise across graph networks, LLMs/agents, and fraud detection.

External job description Job summary Amazon Publisher Services (APS) helps digital publishers around the world build and grow thriving businesses. We provide services and advanced technologies to web, mobile app and advanced TV publishers of all sizes, including many of comScore’s global top 100, to help them monetize their content with demand from multiple programmatic buyers. Our server-side header bidding solutions are fast and reliable across devices, handling billions of queries per day, delivering ads in milliseconds. The result is more profitable advertising for publishers and more relevant ads for customers. As a Data Scientist on this team, you will: • Solve real-world problems by getting and analyzing large amounts of data, diving deep to identify business insights and opportunities, design simulations and experiments, developing statistical and ML models by tailoring to business needs, and collaborating with Scientists, Engineers, BIE's, and Product Managers. • Write code (Python, R, Scala, etc.) to analyze data and build statistical models to solve specific business problems. • Apply statistical and machine learning knowledge to specific business problems and data. • Build decision-making models and propose solution for the business problem you define. • Retrieve, synthesize, and present critical data in a format that is immediately useful to answering specific questions or improving system performance. • Analyze historical data to identify trends and support optimal decision making. • Formalize assumptions about how our systems are expected to work, create statistical definition of the outlier, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed. • Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes. • Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. About the team The Marketplace Services team within Amazon Publisher Services organization primarily focuses on improving monetization for our STV, Web, Mobile and Audio publisher customers. We directly work with 60+ 3p buyers to enable optimal connectivity for publishers to improve their yield. We also own products such as Connections Marketplace (CxM) and Signal IQ that help publishers connect to myriad of 3p and 1p ad tech vendors to boost their bid request quality, while measuring the value of each signal on their bid stream through rigorous A/B testing. Internal job description The candidate would work with Product, Engineering, BIEs and Scientist across Supply and Demand organization to help make APS the best performing supply path for Amazon ads advertiser customers. They would spearhead efforts to conduct experiments alongside demand and measurement teams to identify optimal perfomance path for advertisers while improving APS Share of Wallet. About the team The Marketplace Services team within Amazon Publisher Services organization primarily focuses on improving monetization for our STV, Web, Mobile and Audio publisher customers. We directly work with 60+ 3p buyers to enable optimal connectivity for publishers to improve their yield. We also own products such as Connections Marketplace (CxM) and Signal IQ that help publishers connect to myriad of 3p and 1p ad tech vendors to boost their bid request quality, while measuring the value of each signal on their bid stream through rigorous A/B testing.

Are you a data enthusiast? Are you a creative big thinker who is passionate about using data and optimization tools to direct decision making and solve complex and large-scale challenges? If so, then this position is for you! We are looking for a motivated individual with strong analytic and communication skills to join the effort in evolving the fulfillment center network of tomorrow. At Amazon Worldwide Fulfillment Design and Engineering, we are designing the future and if you are in quest of an iterative fast-paced environment, where you can drive innovation through data visualization products, advance analytics and machine learning at scale, this is your opportunity. In this role, your main focus will be to analyze fulfillment network trends, identify business opportunities, provide project direction, and communicate design and technical requirements within the team and across stakeholder groups. You will own the science vision, lead AI/ML research strategy and roadmap for the org, drive metrics, assist science groups in initial solution design and audit new flow implementations. A successful candidate in this position will have a background in communicating across significant differences, prioritizing competing requests, and quantifying decisions made. Key job responsibilities - Analyze, model and interpret data for Amazon warehouse operational performance. - Predictive analysis using Machine Learning models at scale. - Partner with other scientists and engineers to translate frontier AI research into production-grade tools for process design developments. - Data analysis, modeling, network flow prediction using Excel, Pivot tables, VBA, Tableau, SQL (Amazon Redshift), R, Python. - Innovate and simplify data solutions to standardize and enable automation. - Support Worldwide Engineering teams by providing necessary data for of process flows and selection of various MHE's. - Support process improvement initiatives among site operations, engineering, and corporate systems groups by managing data pipelines. - Develop data and optimization-based solutions to the best-in-class process flow to improve the throughput of the fulfillment facilities. A day in the life Amazon offers a full range of benefits for you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!

Amazon Web Services is looking for world class scientists to join the Security Analytics and AI Research team within AWS Security Services. This group is entrusted with researching and developing core ML and AI solutions for various AWS security services like GuardDuty (https://aws.amazon.com/guardduty/) and Security Hub (https://aws.amazon.com/security-hub/). In this group, you will invent and implement innovative solutions for never-before-solved problems. If you have passion for security and experience with large scale ML/AI problems and/or agentic systems, this will be an exciting opportunity. The AWS Security Services team builds technologies that help customers strengthen their security posture and better meet security requirements in the AWS Cloud. The team interacts with security researchers to codify our own learnings and best practices and make them available for customers. We are building massively scalable and globally distributed security systems to power next generation services. Our team also puts a high value on work-life balance. We thrive to provide a healthy balance between your personal and professional life which is crucial to your happiness and success here. Key job responsibilities - Invent, implement, and deploy state of the art ML/AI algorithms and systems for information security applications. - Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative and business judgment. - Collaborate with software engineering teams to integrate successful experiments into large scale, highly complex production services. - Report results in a scientifically rigorous way. - Interact with security engineers, product managers and related domain experts to dive deep into the types of challenges that we need innovative solutions for.

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。