{
    "schema_version": "via_ai_product_manager_jobs_v1",
    "data_type": "ai_product_manager_jobs",
    "job": {
        "job_title": "平头哥-深度学习推理引擎研发专家-上海",
        "job_level": "专家",AI生成
        "work_category": "全职",AI生成
        "department": "阿里集团",
        "location": "上海",
        "work_mode": "未披露",AI生成
        "job_category": "技术-芯片"AI生成
    },
    "company": {
        "company_name": "平头哥",
        "company_industry": "半导体 / 芯片设计",
        "company_size": "未披露",
        "company_website": "alibaba.com",
        "company_description": "平头哥研发计算、存储及网络芯片，为人工智能基础设施提供支持。"
    },
    "compensation": {
        "salary_range": {
            "min": "",
            "max": "",
            "currency": "未披露",
            "cycle": "未披露"
        },
        "benefits": "未披露"
    },
    "requirements": {
        "working_years": 5,
        "degree": "本科",
        "language": "未披露",AI生成
        "visa_support": "未披露",
        "responsibilities": "1、参与设计和实现推理引擎SDK，提升推理性能、易用性和产品稳定性。（Design and develop inference engine。Focusing on performance、usability and product robustness）\n2、参与设计和实现推理引擎的AI编译。包括图融合、各类图优化、算子优化以及自动化调优等（Design and develop AI Compiling。including fusion，graph optimizations、kernel optimization and auto-tuning）\n3、参与设计和实现推理引擎的运行时系统。包括内存管理以及资源管理等等。实现高效和稳定的稳定性。（Design and develop runtime system，including memory management and resource management）\n4、参与设计和实现大模型的推理优化。基于推理引擎，研发和应用大模型推理优化的技术（Design and optimize LLM inference。Based on inference engine，develop and apply core technology for LLM inference）",
        "requirements_text": "1、 CS/EE相关领域，5+年工作经验(A Masters or higher degree in Computer Engineering, Computer Science, Applied Mathematics or related computing focused degree (or equivalent experience), 5+ years of relevant software development experience)\n2、 熟悉C/C++编程（Excellent C/C++ programming and software design skills, including debugging, performance analysis, and test design）.\n3、 熟悉深度学习框架，如Tensorflow，Pytorch（Familiar with deep learning framework，tensorflow or pytorch etc）\n4、 有较强的技术热情，自我驱动（Proactive and able to work without supervision）\n5、 具备下述条件优先：\na）有AI编译或推理引擎相关研发经验。（Related experience on deep learning compiling or inference engine)\nb) 熟悉CPU/AI架构并有深度学习系统研发经验（Prior experience with performance modelling, profiling, and code optimization in DL）\nc) 熟悉TensorRT、MLIR并跟踪行业技术趋势（Experience on TensorRT or MLIR is highly preferred）",
        "skills": [AI生成
            "C++",
            "PyTorch",
            "TensorFlow",
            "深度学习",
            "C语言"
        ],
        "preferred_qualifications": "5、 具备下述条件优先：；c) 熟悉TensorRT、MLIR并跟踪行业技术趋势（Experience on TensorRT or MLIR is highly preferred）",
        "job_highlights": "未披露"
    },
    "contact": {
        "recruiter_name": "未披露",
        "contact_methods": [
            {
                "type": "apply_link",
                "value": "https://recruitment.t-head.cn/off-campus/position-detail?positionId=740604"
            }
        ]
    },
    "posting": {
        "posting_id": "740604",
        "post_date": "2026-09-15",
        "hire_time": "未披露",
        "update_date": "20260917",
        "job_url": "https://recruitment.t-head.cn/off-campus/position-detail?positionId=740604"
    },
    "fulltext": "平头哥-深度学习推理引擎研发专家-上海\n平头哥-深度学习推理引擎研发专家-上海\n\n基础信息\n职位类别：技术-芯片\n工作地点：上海\n\n职位描述\n1、参与设计和实现推理引擎SDK，提升推理性能、易用性和产品稳定性。（Design and develop inference engine。Focusing on performance、usability and product robustness）\n2、参与设计和实现推理引擎的AI编译。包括图融合、各类图优化、算子优化以及自动化调优等（Design and develop AI Compiling。including fusion，graph optimizations、kernel optimization and auto-tuning）\n3、参与设计和实现推理引擎的运行时系统。包括内存管理以及资源管理等等。实现高效和稳定的稳定性。（Design and develop runtime system，including memory management and resource management）\n4、参与设计和实现大模型的推理优化。基于推理引擎，研发和应用大模型推理优化的技术（Design and optimize LLM inference。Based on inference engine，develop and apply core technology for LLM inference）\n\n职位要求\n1、 CS/EE相关领域，5+年工作经验(A Masters or higher degree in Computer Engineering, Computer Science, Applied Mathematics or related computing focused degree (or equivalent experience), 5+ years of relevant software development experience)\n2、 熟悉C/C++编程（Excellent C/C++ programming and software design skills, including debugging, performance analysis, and test design）.\n3、 熟悉深度学习框架，如Tensorflow，Pytorch（Familiar with deep learning framework，tensorflow or pytorch etc）\n4、 有较强的技术热情，自我驱动（Proactive and able to work without supervision）\n5、 具备下述条件优先：\na）有AI编译或推理引擎相关研发经验。（Related experience on deep learning compiling or inference engine)\nb) 熟悉CPU/AI架构并有深度学习系统研发经验（Prior experience with performance modelling, profiling, and code optimization in DL）\nc) 熟悉TensorRT、MLIR并跟踪行业技术趋势（Experience on TensorRT or MLIR is highly preferred）\n1、参与设计和实现推理引擎SDK，提升推理性能、易用性和产品稳定性。（Design and develop inference engine。Focusing on performance、usability and product robustness）\n2、参与设计和实现推理引擎的AI编译。包括图融合、各类图优化、算子优化以及自动化调优等（Design and develop AI Compiling。including fusion，graph optimizations、kernel optimization and auto-tuning）\n3、参与设计和实现推理引擎的运行时系统。包括内存管理以及资源管理等等。实现高效和稳定的稳定性。（Design and develop runtime system，including memory management and resource management）\n4、参与设计和实现大模型的推理优化。基于推理引擎，研发和应用大模型推理优化的技术（Design and optimize LLM inference。Based on inference engine，develop and apply core technology for LLM inference）\n1、 CS/EE相关领域，5+年工作经验(A Masters or higher degree in Computer Engineering, Computer Science, Applied Mathematics or related computing focused degree (or equivalent experience), 5+ years of relevant software development experience)\n2、 熟悉C/C++编程（Excellent C/C++ programming and software design skills, including debugging, performance analysis, and test design）.\n3、 熟悉深度学习框架，如Tensorflow，Pytorch（Familiar with deep learning framework，tensorflow or pytorch etc）\n4、 有较强的技术热情，自我驱动（Proactive and able to work without supervision）\n5、 具备下述条件优先：\na）有AI编译或推理引擎相关研发经验。（Related experience on deep learning compiling or inference engine)\nb) 熟悉CPU/AI架构并有深度学习系统研发经验（Prior experience with performance modelling, profiling, and code optimization in DL）\nc) 熟悉TensorRT、MLIR并跟踪行业技术趋势（Experience on TensorRT or MLIR is highly preferred）",
    "standard_classification": {
        "std_category_code": "T014",
        "std_aux_codes": [],
        "std_category_status": null,
        "std_taxonomy_version": "20261104"
    }
}