美图Doctoral Research Intern – Management Strategy & Organizational Theory

Team Introduction: The Risk Control R&D Team is dedicated to addressing various challenges posed by malicious activities across ByteDance's products including Douyin and Toutiao. Their work spans multiple domains of risk governance such as content, transactions, traffic, and accounts. By leveraging technologies such as machine learning, multimodal models, and large models, the team strives to understand user behaviors and content, thereby identifying potential risks and issues. By continuously deepening their understanding of business and user behaviors, the team drives innovation in models and algorithms with an aim to build an industry-leading risk control algorithm system. Project Objectives: Optimize and enhance large models' ability to understand and reason about structured data (sequential data, graph data) based on risk control data. Project Necessity: Data in risk control scenarios is primarily structured, while large models have significantly improved their understanding of text and images. Integrating non-text/image structured data from risk control scenarios with large models to enable better comprehension of structured data remains an industry-wide challenge. This involves three key difficulties: 1. How to effectively align structured information with the NLP semantic space, allowing models to simultaneously understand both data structure and semantic information. 2. How to use appropriate instructions to enable large models to interpret structural information in structured data. 3. How to endow large language models with step-by-step reasoning capabilities for graph learning downstream tasks, thereby inferring more complex relationships and attributes. Project Content: Current industry explorations of structured data include: 1. Graph data understanding (e.g., GraphGPT: Enabling large models to read graph data, SIGIR'2024). 2. Graph data RAG (e.g., Microsoft GraphRAG: Unlocking LLM discovery on narrative private data). 3. Sequential data understanding (e.g., StructGPT: A large model reasoning framework for structured data, EMNLP-2023). However, current efforts mainly focus on understanding single-type structured data, and several challenges remain in risk control scenarios: 1. How to effectively fuse and understand various types of structured data, especially the integration of graph and sequential data. 2. Addressing the challenges mentioned in the ""Project Necessity"" section, particularly the step-by-step reasoning capabilities for downstream tasks, which are currently underexplored—especially reasoning over sequential data. Research Directions: 1. Large model structured data understanding 2. Large model structured data RAG 3. Large model thought chains 团队介绍： 风控研发团队致力于解决各个产品（包括抖音、头条等）面临的各种黑灰产对抗问题，涵盖内容、交易、流量、账号等多个方面的风险治理领域。利用机器学习、多模态、大模型等技术对用户行为、内容进行理解从而识别潜在的风险和问题。不断深入理解业务和用户行为，进行模型和算法创新，打造业界领先的风控算法体系。 课题介绍： 1、课题目标：以风控数据为基础，优化提高大模型对于结构化数据（序列数据、图数据）的理解推理能力； 2、课题背景：风控场景下的数据主要为结构化数据，而目前大模型对于文本和图像的理解能力有了很大的提升，如何跟风控场景的非文本、图像数据（结构化数据）结合起来，让大模型能够更好的理解结构化的数据，是一个业界难题。 面临着三大挑战 ： 1）如何有效地将结构化的信息与nlp语义空间进行对齐，使得模型能够同时理解数据结构和语义信息； 2）如何用适当的指令使得大模型理解结构化数据中的结构信息； 3）如何赋予大语言模型图学习下游任务的逐步推理能力，从而逐步推断出更复杂的关系和属性。 3、课题内容：目前业界对结构化数据探索有： 1）图数据理解相关GraphGPT：让大模型读懂图数据（SIGIR'2024）； 2）图数据RAG相关GraphRAG：Unlocking LLM discovery on narrative private data； 3）序列数据理解相关StructGPT：面向结构化数据的大模型推理框架（EMNLP-2023）。 目前的主要工作都是单一结构数据的理解，在风控场景下还面临几个问题： 1）对各种不同种类的的结构化数据融合理解怎么做，特别是融合图和序列数据的数据理解； 2）针对课题必要性中的问题； 3）对于下游任务的推理能力，目前的研究比较少，针对序列数据的推理能力研究非常少。 4、研究方向：大模型结构化数据理解、大模型结构化数据RAG、大模型思维链。

As a Senior Optics Design Engineer, you will engage with an experienced cross-disciplinary staff to conceive and design innovative consumer products. You will work closely with an internal interdisciplinary team, and outside partners to drive key aspects of product definition, execution, and test. You must be responsive, flexible, and able to succeed within an open collaborative peer environment. In this role, you will: • Work with a component engineering team and product engineering team to provide rapid prototyping and characterization of optics or imaging systems • Provide support for modelling and design for prototyping and manufacturing of lightguides and lenses. • Use software tools (LightTools, Zemax etc.) to model system optics characteristics • Work with cross-functional team to draft component specification, design reviews, work with vendors to manufacture parts and validate results by specifying test parameters and methodologies • Communicate and manage overseas component manufacturing vendors A day in the life As a senior optical design engineer, the day begins with reviewing simulation results from the latest lens design iteration. I analyze optical performance metrics like sharpness,aberrations and image quality, ensuring the design meets specifications. Meetings with cross-functional teams follow, where we discuss project timelines, material constraints, and integration challenges. Afternoons are spent refining models in Zemax or CODE V, testing prototypes, or collaborating with the manufacturing team to ensure seamless production. Occasionally, I mentor engineers, reviewing their work and offering guidance. The day wraps up with documenting progress and planning next steps for future optimizations.

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