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Open nowPosted 180 days ago

大语言模型预训练算法工程师

xiaopeng/index1,586 open roles

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

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The clock on this job

Early applications get read.

12.1% 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. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%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（LLM）的底层原理、性能优化和高效预训练充满热情的顶级算法工程师。您将加入我们的核心研发团队，主要负责LLM预训练阶段的算法设计、优化与实现，包括模型架构的探索、训练稳定性的提升、大规模分布式训练的优化等。我们的目标是基于业务需求，设计并训练对硬件计算友好的语言模型，从根本上突破模型的性能和训练效率极限，加速LLM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预训练算法研发与实现：负责1~7B参数级别的Dense/MoE模型以及其他前沿架构在预训练阶段的设计、实验与优化，以提升模型的基础能力和效率。 2. 基准测试与性能优化：负责模型训练过程中的关键性能指标监测与优化，针对小模型特性优化标准化测试流程。通过算法迭代，持续提高模型在理解、推理和泛化能力方面的分数。 3. 训练稳定性与效率提升：负责分析和解决大规模训练过程中的数值不稳定、梯度爆炸/消失等问题，引入和实现如混合精度训练、梯度裁剪、学习率调度等优化策略。 4. 前沿技术追踪与转化：紧密追踪全球LLM预训练、Scaling Law、新型优化器（如AdamW、Lion、Muon）等最新研究进展，评估并将业界顶尖的算法创新快速转化为我们的核心竞争力。 5. 跨团队协作：与数据工程师紧密合作，分析数据对预训练效果的影响，并与系统/硬件工程师协作，共同调优底层计算资源以实现最高训练吞吐。

1. 教育背景：计算机、人工智能、数学、物理等相关专业硕士及以上学位，有顶级会议（如NeurIPS, ICML, ICLR, AAAI等）论文发表经验者优先。 2. 核心算法理解：深入理解Transformer、GPT、DeepSeek、Qwen等主流模型架构的底层数学原理与训练细节，对Linear attention、RMSNorm、DynamicTanh (DyT)、Mixture of Experts (MoE)等LLM底层模块有独到见解。 3. 分布式训练实战经验：具备主导或深度参与LLM预训练的实际经验，熟悉PyTorch、DeepSpeed、Megatron-LM等分布式训练框架。 4. 专业素养：具备严谨的实验设计和结果分析能力，能够主动发现并解决训练过程中的复杂算法问题。 5. 沟通协作：具备优秀的沟通能力和团队合作精神，能够与数据、系统、硬件等不同背景的团队高效协作。 6. 加分项：有使用过大于128张GPU进行LLM预训练、大规模预训练数据合成、硬件友好模型结构设计等相关经验。

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