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Open nowPosted 339 days ago

大模型算法工程师（多模态/语言大模型）

knowinai/referral21 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now大模型算法工程师（多模态/语言大模型）knowinai/referral · Shenzhen, Guangdong, Chinese Mainland

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on knowinai/referral's own form.

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. knowinai/referral postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: posted 339 days ago

knowinai/referral median: 1 days open

The posting

模型微调与对齐 (SFT & RL)： 负责大语言模型（LLM）或多模态模型（VLM）的全流程训练，包括指令微调（SFT）以及强化学习（RL）偏好对齐（PPO/GRPO等），提升模型的指令遵循能力与安全性。 针对垂直领域场景，进行模型适配与持续优化。 数据构建与合成： 负责高质量训练数据的建设，包括文本、图像等多模态数据的清洗与配比优化。 探索合成数据生成技术，通过数据质量提升模型训练效果。 模型部署与推理优化： 负责模型的私有化部署与服务化封装； 应用模型量化、推理加速（如 vLLM）等技术，在保证效果的前提下优化显存占用与响应速度。 . 应用开发与评测： 基于 Prompt Engineering 或 Agent（智能体） 架构解决实际业务问题（如工具调用、复杂规划）。 搭建模型评估体系（Evaluation），量化分析模型在业务场景中的表现并持续迭代。

熟练使用 Python，扎实的算法基础，熟练掌握 PyTorch 等主流深度学习框架。 大模型经验（核心项）： 熟悉主流开源模型（如 Llama、Qwen、DeepSeek等）的架构特点与使用方法。 训练经验： 熟悉 SFT 流程，了解或实践过 RL算法，有 DeepSpeed 等分布式训练经验者优先。 多模态能力： 理解多模态模型（VLM）原理，有图文理解或视频流处理模型的相关经验。 工程与落地： 具备良好的工程落地能力，熟悉模型转换与量化部署工具。 对新技术（如 MoE 架构、长上下文 Long Context 等）保持敏感，能快速复现开源方案。

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