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Open nowPosted 262 days ago

机器学习推理执行引擎研发实习生-Data AML

ByteDance19,260 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now机器学习推理执行引擎研发实习生-Data AMLByteDance · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

11.3% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 27 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.3%3 days
  3. 11.3%7 days
  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: posted 262 days ago

ByteDance median: 27 days open

The posting

ByteIntern：面向2027届毕业生（2026年9月-2027年8月期间毕业），为符合岗位要求的同学提供转正机会。 团队介绍：Data AML是字节跳动的机器学习中台，为抖音/今日头条/西瓜视频等业务提供推荐/广告/CV/语音/NLP的训练和推理系统。为公司内业务部门提供强大的机器学习算力，并在这些业务的问题上研究一些具有通用性和创新性的算法。同时，也通过火山引擎将一些机器学习/推荐系统的核心能力提供给外部企业客户。

1、负责大模型推理引擎底层架构迭代与GPU全链路性能优化，通过算子融合、编译优化等手段，深度优化GPU访存、计算流水线、Stream异步调度，消除推理计算瓶颈，提升单卡推理吞吐、降低推理延迟； 2、适配全系GPU/NPU硬件架构，打磨推理引擎通用性与硬件适配性，搭建高性能、低损耗的大模型推理底层底座； 3、主导大模型推理场景下的分布式并行方案设计、开发与优化，重点落地张量并行（TP）、流水线并行（PP）、序列并行、MoE专家并行等多维度并行策略，解决超大模型多卡拆分部署、跨卡通信开销大、负载不均衡、并行效率低下等核心问题； 4、跟进全球大模型推理、GPU高性能计算、分布式并行、缓存优化等前沿技术，对标vLLM、TensorRT-LLM等主流推理框架，完成方案落地与技术创新，持续迭代优化推理系统性能与成本优势，构建团队核心技术壁垒。

1、2027届本科及以上学历在读； 2、扎实的计算机底层基础，精通C/C++、Python编程，熟练掌握CUDA编程、GPU硬件架构原理，熟悉GPU内存模型、计算调度、通信机制； 3、熟练掌握深度学习各类基础算子的底层开发实现，精通矩阵运算、归一化、激活函数等算子的GPU适配优化，可独立完成算子手写重构、访存优化、向量化加速、精度对齐，保障算子推理的高性能与高稳定性； 4、熟悉深度学习推理编译全链路，理解计算图优化、算子融合、常量折叠、内存复用、调度优化、量化编译等编译技术，能够通过编译层级改造简化推理链路、降低显存占用、减少推理时延，大幅提升模型推理吞吐效率； 5、熟练使用Nsight、Profiler等GPU性能分析工具，可精准定位推理过程中算力浪费、访存阻塞、调度冗余等性能瓶颈，具备软硬件协同优化思维，能够输出系统化优化方案并完成落地迭代，适配产业级高并发、低时延推理业务需求； 6、具备良好的跨团队协作能力、沟通表达能力与文档撰写能力，有较强的责任心，能够推动复杂技术问题的解决与项目落地。

加分项 1、深入理解大模型推理原理，熟练掌握模型并行技术，具备张量并行、流水线并行、序列并行等分布式推理方案落地经验，熟悉多卡通信、负载均衡、并行效率优化方法； 2、有vLLM、SGLang、TensorRT-LLM等主流大模型推理框架二次开发、性能优化经验者优先。

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