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Open nowPosted 180 days ago

大语言模型后训练算法工程师

xiaopeng/index1,586 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now大语言模型后训练算法工程师xiaopeng/index · Shenzhen, Guangdong, Chinese Mainland

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The clock on this job

Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market. xiaopeng/index postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.5%14 days
  5. 35.5%30 days
  6. 45.1%60 days
This job: posted 180 days ago

xiaopeng/index median: 1 days open

The posting

职位描述 我们正在寻找对大语言模型后训练（Post-training）整体效果调优和推理（inference）效能提升充满热情的算法工程师。您将加入我们的核心研发团队，主要负责将团队自行开发/训练的Dense & MoE 模型在后训练阶段提升通用表现(i.e. MMLU, GSM8K, IFEval, Alpaca, etc)。另外，推进监督微调（SFT）、强化学习 (LLM-RL)等架构设计与部署，以及相关基础设施（Infra）的优化。我们的目标是推出对标开源SOTA表现的自研模型，加速模型在人形机器人、自动驾驶、多模态等前沿领域的落地。

工作职责： 1. LLM 后训练与应用落地：负责设计并进行 LLM 的监督微调（SFT）和强化学习（如 RLVR）等实验，以提升模型在特定应用场景（如代码生成、复杂推理、对话、工具调用）的表现。主导模型效果的评估体系搭建和优化，确保模型输出的准确性、安全性和一致性。 2. 云端训练框架与效率优化：主导 LLM 后训练（SFT/RL）流程在云端环境下的效率优化，包括数据加载、多机通信、资源调度、框架稳定性等方面，确保后训练流程的高效、稳定和低成本运行。 3. 跨团队协作：与数据和产品团队紧密合作，一同拟定交付标准，并收集应用层的反馈来指导下一轮模型迭代。

岗位需求 1. 教育背景： 计算机、人工智能、软件工程等相关专业本科及以上学位。 2. 模型实战经验： 具备 LLM（如 LLaMA, GPT, Qwen 等）监督微调（SFT）、强化学习 (LLM-RL)等实务经验。 3. 核心技能： (至少熟悉以下一项) - 有实际的 LLM SFT/RLVR 等微调经验，熟悉主流的微调框架（如 verl, Trinity-RFT, Axolotl, etc）。 - 有大规模应用服务部署经验，熟悉 Kubernetes、Docker 等容器化技术。 - 有 LLM 推理服务部署和优化经验，熟悉 vLLM/SGLang/Ray Serve 等。 - 有将 LLM 应用于人形机器人或自动驾驶相关项目的经验。 4. 编程与框架： 熟练掌握 Python，熟悉 PyTorch 等深度学习框架。具备良好的软件工程和代码习惯。 5. 沟通协作： 具备良好的沟通和团队合作精神，能快速学习和适应新技术。

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