公司网址：https://www.graphcore.cn/

我们相信IPU智能处理器技术将成为全球机器智能计算标准。无论您是医学研究人员、机器人专家还是自动驾驶汽车制造商，Graphcore IPU都将在所有行业中实现变革。

我们构建了一个全新的处理器，即IPU。它专门为AI计算而设计。IPU独特的架构使AI研究人员能够开展那些使用当前技术无法实现的、全新类型的工作，推动机器智能新发展。

职位：机器学习算法工程师 (Machine Learning Algorithm Engineer)

职责：
o    基于Graphcore IPU AI算法模型的开发与研究， 包括计算机视觉, 语音, NLP或者强化学习模型方向
o    围绕软硬件一体化计算机系统设计， 研究和改进机器学习或者数值计算的应用模型，算法的实现与全流程优化策略
o    参与面向应用的深度学习，高性能计算的算法实现与优化，计算系统分析等技术领域相关项目的研究和原型开发
要求：
o    熟练使用Python, 有扎实的编程基础，良好的编程习惯
o    有在计算机视觉, 语音, NLP深度学习或者强化学习模型方面有开发研究经验
o    熟悉机器学习基础算法，有基于Tensorflow/Pytorch/MXNET/PaddlePaddle等深度学习框架的实际算法开发经验
o    对业界学术界最新模型保持关注和好奇心，了解目前最新模型的基本结构和原理
o    快速学习的能力
o    优秀的团队合作能力，自我驱动和专注结果
o    数理专业，计算机或者电子类工程专业学士及以上学历


Position: Machine Learning Algorithm Engineer (Machine Learning Algorithm Engineer)

Responsibilities:
o Development and research based on Graphcore IPU AI algorithm model,
o Focusing on the design of integrated software and hardware computer systems, research and improvement of machine learning or numerical computing application models, algorithm implementation and overall process optimization strategies
o Participate in the research and prototype development of application-oriented deep learning, high-performance computing algorithm implementation and optimization, computing system analysis and other technical fields
Requirements:
o Familiar with Python, have a solid programming foundation and good programming habits
o Maintain attention and curiosity about the latest models in the industry and academia, and understand the basic structure and principles of the current latest models
o Familiar with basic machine learning algorithms, and have actual algorithm development experience based on deep learning frameworks such as Tensorflow/Pytorch/MXNET/PaddlePaddle
o Ability to learn quickly
o Excellent teamwork ability, self-driven and focused on results
o Bachelor degree or above in mathematics and science, computer or electronic engineering

职位：机器学习模型优化工程师(Machine Learning Optimization Engineer)

职责：
o    开发和优化基于IPU硬件架构的基础算子和自定义算子
o    开发和优化基本IPU硬件架构的高性能计算的算法
o    开发和优化基于IPU硬件架构的线性计算公共库
要求：
o    精通 C++, 熟练使用python
o    在并行编程，并行编译，AI框架，AI编译器，编译优化，性能调优，AI 加速器中任何领域有实际经验
o    精通GPU处理器架构，精通CUDA， cuDNN， 在深度学习计算框架等领域有检验者优先
o    有AI编译器开发经验者优先，熟悉XLA/TVM/MLIR者优先
o    计算机，电子，通信或者数理专业本科及以上学历

Position: Machine Learning Optimization Engineer (Machine Learning Optimization Engineer)

Responsibilities:
o Develop and optimize basic operators and custom operators based on IPU hardware architecture
o Develop and optimize high-performance computing algorithms for basic IPU hardware architecture
o Develop and optimize linear computing public libraries based on IPU hardware architecture
Requirements:
o Proficient in C++, familiar with python
o Have practical experience in any field of parallel programming, parallel compilation, AI framework, AI compiler, compilation optimization, performance tuning, and AI accelerator
o Proficient in GPU processor architecture, proficient in CUDA, cuDNN, in deep learning computing framework and other fields are preferred
o Experience in AI compiler development is preferred, familiar with XLA/TVM/MLIR is preferred
o Bachelor degree or above in computer, electronics, communication or mathematics

职位：机器学习框架工程师(Machine Learning Framework Engineer)

职责：
o    研究和开发AI编译器和自动优化工具的关键技术
o    研究和开发软硬件协同设计方法和相关工具
o    针对IPU芯片构架，研发新的AI编译优化方法
o    参与AI编译器开源社区的工作
要求：
o    精通 C++ ，熟练使用Python， 具有快速实现原型的能力
o    理解深度学习训练推理框架的基本原理
o    有AI 处理器适配PaddlePaddle/pyTorch/TensorFlow主流深度学习框架的经验
o    计算机，电子，通信或者数理专业本科及以上学历
o    优秀的团队合作能力， 自我激励和专注结果

Position: Machine Learning Framework Engineer (Machine Learning Framework Engineer)

Responsibilities:
o Research and develop key technologies of AI compilers and automatic optimization tools
o Research and develop software and hardware co-design methods and related tools
o For the IPU chip architecture, develop new AI compilation optimization methods
o Participate in the work of the AI compiler open source community
Requirements:
o Proficient in C++, proficient in Python, and capable of rapid prototype
o Understand the basic principles of deep learning training inference framework
o Experience with AI processor adapting PaddlePaddle/pyTorch/TensorFlow mainstream deep learning framework
o Bachelor degree or above in computer, electronics, communication or mathematics
o Excellent teamwork ability, self-motivation and results-driven

职位：机器学习系统工程师(Machine Learning System Engineer)

职责：
o    开发和优化基于IPU集群的分布式训练，譬如对模型并行，数据并行做系统级的优化
o    对ML 整个软件栈做系统集成和优化，例如集成文件系统， 参数服务器和AutoML平台
要求：
o    2年以上分布式系统相关经验，复杂系统软件的设计和调试能力，熟知常见的设计模式和架构的平衡
o    精通分布式计算框架，熟悉Hadoop/HIVE/MPI/Spark/TensorFlow等分布式计算框架，了解常见深度学习框架引擎
o    在机器学习，深度学习，大规模分布式机器学习以及在搜索，广告，推荐，机器翻译等领域有相关经验者优先



Position: Machine Learning System Engineer (Machine Learning System Engineer)

Responsibilities:
o Develop and optimize distributed training based on IPU clusters, such as system-level optimization for model parallelism and data parallelism
o System integration and optimization of the entire software stack of ML, such as integrated file system, parameter server and AutoML platform
Requirements:
o More than 2 years of relevant experience in distributed systems, design and debugging capabilities of complex system software, familiar with common design patterns and architecture balance
o Proficient in distributed computing frameworks, familiar with distributed computing frameworks such as Hadoop/HIVE/MPI/Spark/TensorFlow, and familiar with common deep learning framework engines
o Those who have relevant experience in machine learning, deep learning, large-scale distributed machine learning and search, advertising, recommendation, machine translation and other fields are preferred


工作地点：北京，上海，深圳 三地任选其一，中英文简历请发送至stanleyl@graphcore.ai
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