希音大模型应用工程师 (LLM Application Engineer)

图研发团队负责美图系列产品的全链路技术开发与中台体系建设。我们聚焦AI影像、多端协同与数据智能，用扎实的技术将创意转化为稳定优美的亿级用户产品体验。期待你加入，用代码赋能创意。 Our R&D Team drives full-stack development for Meitu's product suite and platform infrastructure. Specializing in AI imaging, multi-end collaboration, and data intelligence, we turn ideas into stable, elegant experiences for millions of users. Join us to code the future of creativity. 岗位名称：测试开发工程师 工作地点：厦门 岗位职责： ● 参与Windows / Mac / Android / iOS 平台下的自动化测试和专项测试的相关工作 ● 追踪并分析线上线下故障，推动问题的合理解决，提升项目质量 ● 根据公司产品特点和业务需求，对测试工具和方式进行优化，包括但不限于研发和维护内部工具、平台、系统和框架 ● 学习和研究新技术并落地以提高测试效率和质量

Team Introduction: TikTok Content Security Algorithm Research Team The International Content Safety Algorithm Research Team is dedicated to maintaining a safe and trustworthy environment for users of ByteDance's international products. We develop and iterate on machine learning models and information systems to identify risks earlier, respond to incidents faster, and monitor potential threats more effectively. The team also leads the development of foundational large models for products. In the R&D process, we tackle key challenges such as data compliance, model reasoning capability, and multilingual performance optimization. Our goal is to build secure, compliant, and high-performance models that empower various business scenarios across the platform, including content moderation, search, and recommendation. Research Project Background: In recent years, Large Language Models (LLMs) have achieved remarkable progress across various domains of natural language processing (NLP) and artificial intelligence. These models have demonstrated impressive capabilities in tasks such as language generation, question answering, and text translation. However, reasoning remains a key area for further improvement. Current approaches to enhancing reasoning abilities often rely on large amounts of Supervised Fine-Tuning (SFT) data. However, acquiring such high-quality SFT data is expensive and poses a significant barrier to scalable model development and deployment. To address this, OpenAI's o1 series of models have made progress by increasing the length of the Chain-of-Thought (CoT) reasoning process. While this technique has proven effective, how to efficiently scale this approach in practical testing remains an open question. Recent research has explored alternative methods such as Process-based Reward Model (PRM), Reinforcement Learning (RL), and Monte Carlo Tree Search (MCTS) to improve reasoning. However, these approaches still fall short of the general reasoning performance achieved by OpenAI's o1 series of models. Notably, the recent DeepSeek R1 paper suggests that pure RL methods can enable LLM to autonomously develop reasoning skills without relying on the expensive SFT data, revealing the substantial potential of RL in advancing LLM capabilities. 团队介绍： 国际化内容安全算法研究团队致力于为字节跳动国际化产品的用户维护安全可信赖环境，通过开发、迭代机器学习模型和信息系统以更早、更快发掘风险、监控风险、响应紧急事件，团队同时负责产品基座大模型的研发，我们在研发过程中需要解决数据合规、模型推理能力、多语种性能优化等方面的问题，从而为平台上的内容审核、搜索、推荐等多项业务提供安全合规，性能优越的基座模型。 课题介绍: 课题背景： 近年来，大规模语言模型（Large Language Models, LLM）在自然语言处理和人工智能的各个领域都取得了显著的进展。这些模型展示了强大的能力，例如在生成语言、回答问题、翻译文本等任务上表现优异。然而，LLM 的推理能力仍有很大的提升空间。在现有的研究中，通常依赖于大量的监督微调（Supervised Fine-Tuning, SFT）数据来增强模型的推理性能。然而，高质量 SFT 数据的获取成本高昂，这对模型的开发和应用带来了极大的限制。 为了提升推理能力，OpenAI 的 o1 系列模型通过增加思维链（Chain-of-Thought, CoT）的推理过程长度取得了一定的成功。这种方法虽然有效，但在实际测试时如何高效地进行扩展仍是一个开放的问题。一些研究尝试使用基于过程的奖励模型（Process-based Reward Model, PRM）、强化学习（Reinforcement Learning, RL）以及蒙特卡洛树搜索算法（Monte Carlo Tree Search, MCTS）等方法来解决推理问题，然而这些方法尚未能达到 OpenAI o1 系列模型的通用推理性能水平。最近deepseek r1在论文中提到通过纯强化学习的方法，可以使得 LLM 自主发展推理能力，而无需依赖昂贵的 SFT 数据。这一系列的工作都揭示着强化学习对LLM的巨大潜力。 课题挑战： 1、Reward模型的设计：在强化学习过程中，设计一个合适的reward模型是关键。Reward模型需要准确地反映推理过程的效果，并引导模型逐步提升其推理能力。这不仅要求对不同任务精准设定评估标准，还要确保reward模型能够在训练过程中动态调整，以适应模型性能的变化和提高。 2、稳定的训练过程：在缺乏高质量SFT数据的情况下，如何确保强化学习过程中的稳定训练是一个重大挑战。强化学习过程通常涉及大量的探索和试错，这可能导致训练不稳定甚至模型性能下降。需要开发具有鲁棒性的训练方法，以保证模型在训练过程中的稳定性和效果。 3、如何从数学和代码任务上拓展到自然语言任务上：现有的推理强化方法主要应用在数学和代码这些CoT数据量相对丰富的任务上。然而，自然语言任务的开放性和复杂性更高，如何将成功的RL策略从这些相对简单的任务拓展到自然语言处理任务上，要求对数据处理和RL方法进行深入的研究和创新，以实现跨任务的通用推理能力。 4、推理效率的提升：在保证推理性能的前提下，提升推理效率也是一个重要挑战。推理过程的效率直接影响到模型在实际应用中的可用性和经济性。可以考虑利用知识蒸馏技术，将复杂模型的知识传递给较小的模型，以减少计算资源消耗。另外，使用长思维链（Long Chain-of-Thought, Long-CoT）技术来改进短思维链（Short-CoT）模型，也是一种潜在的方法，以在保证推理质量的同时提升推理速度。

Team Introduction: Dedicated to building an industry-leading large-model dialogue system, the team serves hundreds of millions of daily active users, with application scenarios covering the entire Douyin e-commerce ecosystem. This includes core business scenarios such as platform customer service, platform merchant service, merchant customer service, influencer customer service, and innovative intelligent shopping guides. Through continuous technological innovation and optimization, the team has successfully established a complete intelligent dialogue solution, delivering significant efficiency improvements and user experience enhancements to e-commerce operations. Research Objectives: Develop an LLM-based customer service chatbot for TikTok and Douyin E-commerce, enabling intelligent customer service interactions. The LLM will handle the entire user inquiry process, including request clarification, solution negotiation, and execution. Necessity: LLM's strong conversational and reasoning abilities make it especially suitable for intelligent customer service, capable of potentially reaching the service standards of excellent human representatives. Research Content: Design a multi-agent framework based on LLM, integrating planning-agent, reply-agent, and tool-agent. Each agent will specialize in different functions, working collaboratively to manage the complete service process—from issue identification and solution negotiation to solution implementation and feedback. 1) Reply-agent ensures the proposed solutions comply with platform policies and service guidelines, avoids excessive improvisation or hallucinations, and maintains smooth communication and negotiation with the user. 2) Planning-agent identifies user demands and problem scenarios, sourcing relevant service guidelines and constraints as well as recognizing risk scenarios. 3) Tool-agent validates the legality of tool usage, accurately interprets the results from tool interactions, and manages execution dependencies of various actions. Research Challenges: Compliance with service guidelines: Ensuring the chatbot's solutions adhere to platform service guidelines (such as available refund within xx days of parcel arrival and coupon limits per user per week). Dynamic feedback adaptation: Static adherence to service rules and providing fixed solutions can limit the flexibility of reply-agents, preventing them from acting like excellent human customer service representatives. By enabling reply-agents to interact in real-time with their environment, considering user's behavioral trends, demands expressed during inquiries, and feedback on proposed solutions, personalized service can be provided. This approach fosters adaptive responses and progressive services and solutions, closely mirroring the flexibility and excellence of human customer service. Self-reflection: Employing LLM's capabilities to understand, analyze, and evaluate its own behavior, fostering self-supervision and decision refinement through reflection on outputs, particularly with complex and ambiguous tasks. Complex image processing: Handling scenarios involving numerous complex images (including shipping order photos, bank transaction screenshots, images of damaged goods received, and seller qualification certifications). These images contain key information crucial to enhancing the chatbot's problem resolution capabilities. 团队介绍： 智能对话团队，致力于打造业界领先的大模型对话系统。团队服务的日活用户超过数亿，应用场景覆盖抖音电商全链路，包括平台客服、平台商服、商家客服、达人客服，以及创新的智能导购等核心业务场景，通过持续的技术创新和优化，成功构建了一套完整的智能对话解决方案，为电商业务带来了显著的效率提升和用户体验改善。 课题目标： 构建基于LLM的电商客服机器人（Chatbot），服务TikTok和抖音电商智能客服场景，由LLM完成一次用户进线的完整接待过程，包括诉求澄清、方案协商、方案执行等阶段。 必要性： LLM具有强大的对话和推理能力，智能客服是LLM能够发挥价值的最典型场景，有机会能够达到匹配优秀人工客服的服务能力。 课题内容： 设计一个基于LLM 的 multi-agent framework，将 planning-agent、reply-agent、tool-agent 集成到一起，每个 agent 负责不同能力，互相协同，完成从问题定位、方案协商，到方案执行、结果反馈等服务全流程。reply agent 需要确保给用户提供的方案是符合平台的相关政策和service policy的，不自行过度发挥、不出现幻觉，顺滑的完成和用户的沟通协商过程；planning agent 完成定位用户诉求和问题场景，以便从外部获取该场景的服务准则和约束，如何识别风险场景；tool agent 需要确保工具调用的合法性、接收和解析工具调用的返回结果，另外一些动作的执行存在前后依赖的问题。 课题挑战： 1、遵循服务准则：如何确保方案Chatbot提供的方案是follow平台服务准则的，例如到货xx天之内可以申请退款、同一用户一星期内最多发送xx额度的优惠券； 2、感知环境反馈：reply agent如果只能死板的follow当前场景服务准则，提供一层不变的方案，是无法像优秀客服一样做到灵活变通的。让Agent能够实时的和环境打通，通过结合当前用户进线前的行为动线、进线后表达的诉求和用户对 agent 提供方案的反馈，为用户提供个性化的服务，对用户的实时反馈有响应，像优秀客服一样能随机应变，递进式的提供服务和解决方案； 3、进行自我反思：利用LLM理解、分析和评价其自身的行为，使LLM能够自我监督，通过对自身输出的反思，改进其所做的决策，以便在处理复杂、有歧义的任务时，能有更好的表现； 4、复杂图片理解：电商场景存在大量复杂的图片，包括运费订单实拍图、银行流水截图、买家收货缺件破损的、商家各类资质证明等，这类图片往往包含重要的信息，对提升Chatbot解决能力非常重要。

需要英文简历 1. Participate in technical support and solution design for the company's core products, including computer vision, AI agents, and Intelligent robot, assisting in customer requirement investigation, feature definition, and iteration planning for AI scenarios. 2. Engage in end-to-end management of AI projects, including project planning, resource coordination, progress tracking, and risk identification, helping to ensure high-quality project delivery. 3. Assist in customer interactions, participate in business pain point analysis, and collaborate on designing technically feasible and user-friendly AI solutions while helping to maintain customer relationships. 4. Collaborate with algorithms, software development, and business teams to promote product feature implementation and project delivery, enhancing cross-team efficiency. 5. Stay updated on cutting-edge AI technologies and trends, incorporating user feedback and business data to contribute to product optimization and innovation. 1. 参与公司AI视觉、AI Agent及智能机器人等核心产品的技术支持与解决方案设计，协助完成AI场景的需求调研、功能定义及迭代规划； 2. 参与AI类项目的全流程管理，包括项目计划制定、资源协调、进度跟踪与风险识别，协助推动项目高质量交付； 3. 协助与业务对接，参与痛点分析，共同制定技术可行、体验优秀的AI解决方案，并协助维护与业务方的关系； 4. 与算法、软件开发及业务团队协同工作，推动产品功能落地与项目交付，提升多团队协作效率； 5. 跟踪AI前沿技术与发展趋势，结合用户反馈与业务数据，参与产品优化与创新迭代。