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Open nowPosted 89 days ago

高性能计算架构工程师 - AI for Science

ByteDance19,257 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now高性能计算架构工程师 - AI for ScienceByteDance · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 16 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 89 days ago

ByteDance median: 16 days open

The posting

团队介绍：AI for Science 团队专注于科学计算与物质发现领域的前瞻技术探索，围绕生物和材料领域基础模型、量子化学、AI分子动力学、高通量实验技术等方向，用 AI 推动科学领域的研究范式突破，也在实际应用场景中创造价值。我们希望用 AI 赋能科学，探索 AI 在科研领域的变革，拓展人类认知边界！

1、参与AI4S高性能计算框架与代码库的设计、开发和维护，设计实现训练、推理、数据处理、模型执行等基础模块，持续完善代码库的模块化、可测试性和可维护性，并与算法、科学计算及业务团队协作，将研究原型沉淀为可复用的工程框架和平台能力； 2、参与机器学习力场模型训练与推理框架及LLM for Science等方向的模型基础设施建设，提升模型在单机、多卡及分布式环境下的运行效率和工程可用性，解决模型规模扩展、计算效率、稳定性及资源利用率等工程问题； 3、参与分子动力学计算引擎的优化与改造，包括计算模块开发、接口设计、并行计算支持及与机器学习模型的集成。

1、2027届获得硕士及以上学位，计算机、软件工程、人工智能或相关专业优先； 2、具备扎实的软件工程能力，能够参与较大规模代码库的开发、测试、文档建设和长期维护； 3、熟悉PyTorch，具有深度学习模型训练或推理相关项目经验；参与过PyTorch、Megatron-LM、DeepSpeed、vLLM或类似训练、推理框架的开发或深度使用；具有分布式训练、模型训练框架、推理框架或AI Infra相关实践经验；具有多机多卡训练、通信优化、并行策略设计或分布式系统开发经验； 4、具备较强的问题分析、调试和工程落地能力，能够定位并解决复杂系统中的性能、稳定性或可扩展性问题； 5、对AI4S、高性能计算和科学计算方向有兴趣，愿意持续学习相关领域知识；具有机器学习力场、分子动力学、OpenMM、LAMMPS等AI4S或科学计算项目经验；对LLM for Science、科学智能基础设施等方向有实践或深入思考；具有CUDA、Triton、MPI、NCCL或其他高性能计算相关实践经验；具有C++、计算引擎、数值计算库或高性能计算代码库开发经验； 6、参与过开源项目，具备代码贡献、模块维护或社区协作经验。

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