贝壳大模型算法高阶(J66332)
社招全职智能研发中心地点:北京状态:招聘
任职要求
1. 熟悉大模型的原理,具备精调、强化学习等方面的经验; 2. 熟悉自然语言处理常见算法与模型,具备深度学习技术再NLP领域的应用实践; 3. 具备良好的编程实现能力,熟练掌握python、pytorch、Transformers等机器学习套件; 4. 具有良好的逻辑思维和问题解决能力,具备强烈的进取心、求知欲,热衷于追求技术创新; 5. 有NLP顶会论文发表的very优先考虑。
工作职责
岗位亮点 1. 团队集前沿技术探索、落地与一身,能够极大的发挥技术价值,未来拥有广阔的前景; 2. 部门业务快速发展,业务落地场景丰富,复杂度高,挑战大,你将拥有快速成长的空间; 工作职责 1. 参与大模型在领域的落地,用LLM洞察分析大规模的数据; 2. 基于大模型、agent思想,研发全新的对话交互项目; 3. 不断探索技术新领域,推动技术能力的沉淀、技术氛围、技术影响力建设;
包括英文材料
大模型+
https://www.youtube.com/watch?v=xZDB1naRUlk
You will build projects with LLMs that will enable you to create dynamic interfaces, interact with vast amounts of text data, and even empower LLMs with the capability to browse the internet for research papers.
https://www.youtube.com/watch?v=zjkBMFhNj_g
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.
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.
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/
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
Python+
https://liaoxuefeng.com/books/python/introduction/index.html
中文,免费,零起点,完整示例,基于最新的Python 3版本。
https://www.learnpython.org/
a free interactive Python tutorial for people who want to learn Python, fast.
https://www.youtube.com/watch?v=K5KVEU3aaeQ
Master Python from scratch 🚀 No fluff—just clear, practical coding skills to kickstart your journey!
https://www.youtube.com/watch?v=rfscVS0vtbw
This course will give you a full introduction into all of the core concepts in python.
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.
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.
相关职位
社招核心本地商业-基
1. 从flops efficiency角度,对大模型预训练过程进行分析与探索,包括不限于对scaling law的研究,数据的认知实验,MoE等模型架构设计,以及其他工程与算法结合的ml sys相关优化等。 2. 负责原生多模态大模型的构建与预训练,包括各模态分词器设计,合成数据策略,以及跨模态融合训练策略等,提升原生多模态模型在视觉/语音的理解与生成任务上的能力。 3. 进行大模型reasoning能力的相关研究,研究pre-train / test-time compute对于模型推理能力的影响,借助SFT / DPO / self-play等手段,提高模型在代码数学等推理任务上的表现。 4. 通过post-training激发模型在各下游任务上的能力,从而构建具有通用问题解决能力的agent。
更新于 2025-04-27
社招3年以上混元-模型算法技
1.负责大语言模型(LLM)规划、推理、反思能力的研究,提升大语言模型的高阶推理能力; 2.跟进推理领域的前沿技术,将其应用于混元大模型基座,持续提升大模型的推理能力。
更新于 2025-07-26
社招
1. 开发通用型具身算法并应用于人形机器人场景任务,具备物体泛化、任务泛化、场景泛化能力; 2. 研究多模态具身大模型,具备视觉、触觉、语言感知和决策能力,控制机器人完成开放世界的物理交互;
更新于 2025-04-28