字节跳动豆包大模型算法工程师（AIGC Agent方向） - 火山方舟
社招全职A106181B地点：深圳状态：招聘♡ 收藏
工作描述
任职要求 1、熟悉LLM/VLM/Agent相关技术，具备大模型训练、后训练或Agent优化相关经验； 2、具备模型迭代经验，深入参与以下任一方向的研究或工程实践： 1）RFT/RLHF/GRPO/OPD等模型强化与对齐优化技术； 2）SFT训练体系建设与高质量数据构建； 3）Agent RL能力优化； 4）多模态模型后训练与能力提升。 加分项 1、具备AIGC、多模态生成、智能体等方向实践经验，对AI创作场景有长期兴趣和深入理解； 2、具备Agent Post-training、Agent Evaluation、Agent Loop优化经验； 3、具备Multi-Agent、Context Management、Memory、自我进化Agent等方向的研究或工程经验； 4、熟悉图像/视频生成模型技术，包括Diffusion…
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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
AI agent+
https://www.ibm.com/think/ai-agents
Your one-stop resource for gaining in-depth knowledge and hands-on applications of AI agents.
RLHF+
[英文] What is RLHF?
https://aws.amazon.com/what-is/reinforcement-learning-from-human-feedback/
Reinforcement learning from human feedback (RLHF) is a machine learning (ML) technique that uses human feedback to optimize ML models to self-learn more efficiently.
https://www.ibm.com/think/topics/rlhf
Reinforcement learning from human feedback (RLHF) is a machine learning technique in which a “reward model” is trained with direct human feedback, then used to optimize the performance of an artificial intelligence agent through reinforcement learning.
GRPO+
https://cameronrwolfe.substack.com/p/grpo
Most early work on RL for LLMs used Proximal Policy Optimization (PPO) as the default RL optimizer, but recent reasoning research relies upon Group Relative Policy Optimization (GRPO).
SFT+
https://cameronrwolfe.substack.com/p/understanding-and-using-supervised
Understanding how SFT works from the idea to a working implementation...
AIGC+
https://ui.adsabs.harvard.edu/abs/2023arXiv230406632W/abstract
To address the challenges of digital intelligence in the digital economy, artificial intelligence-generated content (AIGC) has emerged.
智能体+
https://learn.microsoft.com/en-us/shows/ai-agents-for-beginners/
In this 10-lesson course we take you from concept to code while covering the fundamentals of building AI agents.
https://www.ibm.com/think/ai-agents
Your one-stop resource for gaining in-depth knowledge and hands-on applications of AI agents.
Transformer+
https://huggingface.co/learn/llm-course/en/chapter1/4
Breaking down how Large Language Models work, visualizing how data flows through.
https://poloclub.github.io/transformer-explainer/
An interactive visualization tool showing you how transformer models work in large language models (LLM) like GPT.
https://www.youtube.com/watch?v=wjZofJX0v4M
Breaking down how Large Language Models work, visualizing how data flows through.
NeurIPS+
https://neurips.cc/
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