快手风控算法工程师（大模型）-【商业化】
社招全职3-5年J0011地点：北京状态：招聘
任职要求
1、熟练掌握自然语言处理、计算机视觉，强化学习，推荐系统等常用算法，对大规模预训练模型和下游应用，有深入实践者优先； 2、有扎实的机器学习实践经验，熟悉一种主流的机器学习框架（PyTorch，Tensorflow，MXNet等）； 3、熟悉LLM前沿技术，参与过LLM/SD等大模型算法和工程链路研发，有大模型分布式训练、模型…
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工作职责
1、负责大模型在风控场景下应用落地，包括在业务场景下模型的预训练、微调、评估、部署，提升模型检测效果； 2、利用LLM前沿技术方向，负责短视频内容生成caption、视频/直播内容理解(风险分级、内容结构化、智能标签/摘要，优质文案)等。
包括英文材料
NLP+
https://www.youtube.com/watch?v=fNxaJsNG3-s&list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S
Welcome to Zero to Hero for Natural Language Processing using TensorFlow!
https://www.youtube.com/watch?v=R-AG4-qZs1A&list=PLeo1K3hjS3uuvuAXhYjV2lMEShq2UYSwX
Natural Language Processing tutorial for beginners series in Python.
https://www.youtube.com/watch?v=rmVRLeJRkl4&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4
The foundations of the effective modern methods for deep learning applied to NLP.
OpenCV+
https://learnopencv.com/getting-started-with-opencv/
At LearnOpenCV we are on a mission to educate the global workforce in computer vision and AI.
https://opencv.org/university/free-opencv-course/
This free OpenCV course will teach you how to manipulate images and videos, and detect objects and faces, among other exciting topics in just about 3 hours.
强化学习+
https://cloud.google.com/discover/what-is-reinforcement-learning?hl=en
Reinforcement learning (RL) is a type of machine learning where an "agent" learns optimal behavior through interaction with its environment.
https://huggingface.co/learn/deep-rl-course/unit0/introduction
This course will teach you about Deep Reinforcement Learning from beginner to expert. It’s completely free and open-source!
https://www.kaggle.com/learn/intro-to-game-ai-and-reinforcement-learning
Build your own video game bots, using classic and cutting-edge algorithms.
推荐系统+
[英文] Recommender Systems
https://www.d2l.ai/chapter_recommender-systems/index.html
Recommender systems are widely employed in industry and are ubiquitous in our daily lives.
算法+
https://roadmap.sh/datastructures-and-algorithms
Step by step guide to learn Data Structures and Algorithms in 2025
https://www.hellointerview.com/learn/code
A visual guide to the most important patterns and approaches for the coding interview.
https://www.w3schools.com/dsa/
机器学习+
https://www.youtube.com/watch?v=0oyDqO8PjIg
Learn about machine learning and AI with this comprehensive 11-hour course from @LunarTech_ai.
https://www.youtube.com/watch?v=i_LwzRVP7bg
Learn Machine Learning in a way that is accessible to absolute beginners.
https://www.youtube.com/watch?v=NWONeJKn6kc
Learn the theory and practical application of machine learning concepts in this comprehensive course for beginners.
https://www.youtube.com/watch?v=PcbuKRNtCUc
Learn about all the most important concepts and terms related to machine learning and AI.
PyTorch+
https://datawhalechina.github.io/thorough-pytorch/
PyTorch是利用深度学习进行数据科学研究的重要工具，在灵活性、可读性和性能上都具备相当的优势，近年来已成为学术界实现深度学习算法最常用的框架。
https://www.youtube.com/watch?v=V_xro1bcAuA
Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in Python.
TensorFlow+
https://www.youtube.com/watch?v=tpCFfeUEGs8
Ready to learn the fundamentals of TensorFlow and deep learning with Python? Well, you’ve come to the right place.
https://www.youtube.com/watch?v=ZUKz4125WNI
This part continues right where part one left off so get that Google Colab window open and get ready to write plenty more TensorFlow code.
MXNet+
https://www.tutorialspoint.com/apache_mxnet/index.htm
Apache MXNet is a powerful deep learning framework that supports both symbolic and imperative programming.
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更新于 2025-12-26北京
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更新于 2026-04-07北京
社招技术
1、核心算法研究与设计：主导金融风控领域RAG-Agent的核心算法研究与创新，包括但不限于检索算法优化、重排序模型设计、大模型上下文增强与生成控制策略。 2、RAG全链路算法优化：研发面向非结构化金融文本的智能切片算法、查询理解与重写模型、多路召回融合策略，重点攻克多阶段排序模型，优化混合检索结果的精准度与相关性；研究与实施大模型在金融风控场景下的高效微调方案、提示工程范式与推理链设计，致力于提升生成内容的准确性、可追溯性与逻辑严谨性；构建领域定量的RAG评估体系，设计自动化评估算法与badcase分析机制，驱动算法闭环迭代。 3、前沿探索与业务赋能：探索Agent规划与决策、多模态信息融合、长上下文建模等前沿方向在风控场景的算法落地，并将核心算法能力转化为业务价值。
更新于 2026-06-11北京
社招3-5年内容理解
1、负责将内容理解、机器学习、流量调控算法能力落地于小红书笔记/评论/直播/商品/账号/广告生态治理场景，技术驱动小红书生态和画风优化 2、具体负责多模态识别、计算机视觉、自然语言处理、语言/多模态大模型、决策树模型等算法的研发迭代，通过样本去噪、样本生成、模型创新、自监督、few-shot等手段持续提高治理效果
更新于 2026-04-08北京|上海|杭州