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Open nowPosted 239 days ago

深度学习性能优化工程师

li/referral788 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now深度学习性能优化工程师li/referral · Shanghai, 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 li/referral's own form.

The reply lands in your private mailbox

3×more interviews than doing it yourself with ChatGPT.

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. li/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. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 239 days ago

li/referral median: 1 days open

The posting

基于ExaFLOPS高性能集群构建大规模机器学习平台，结合底层硬件架构、深度学习框架和AI编译等相关技术，为自动驾驶、CV、LLM、RL等算法模型的训练、推理和部署提供全链路性能加速。

1.识别和定位深度学习模型训练推理和系统的性能瓶颈，优化CPU/GPU/内存/通信等资源利用率，提升并行计算并发效率； 2.研究深度学习和性能加速的最新发展趋势，将前沿的算法和优化技术应用到实际生产实践中。

1.计算机、人工智能、应用数学等相关专业硕士以上学历，5年研发经验； 2.扎实的代码能力，有较强的软件架构设计能力，熟练掌握C/C++/Python编程语言，熟练掌握CUDA/OpenCL/SYCL等其中一种编程语言； 3.深度理解自动驾驶、CV、NLP/LLM、RL等领域相关算法模型和常用开发工具包； 4.熟悉TensorFlow、PyTorch等框架，熟悉掌握深度学习性能加速技术，包括但不限于计算图优化、低精度优化、算子加速，以及DeepSpeed、Megatron等并行加速技术。

加分项： 1.熟悉AI编译器，有LLVM、TVM、MLIR、XLA 等编译开发经验者优先； 2.熟悉大规模并行计算集群、分布式存储基础架构、高性能Collective 通信原语等优先； 3.熟悉计算机体系结构，对CPU/GPU/DSA微架构或内存子系统有深刻理解优先； 4.有操作系统内核、虚拟化、系统库、编译器等底层系统开发经验优。

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    The review screen in CV Rocket: how each CV was read, round by round.

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