远程工作雷达

AI工程师

AI Engineer

AI开发工程限定地区（需当地身份）
公司Team Up Services
薪资未公开
工作地点United States
地域资格限定地区（需当地身份）
时区要求日间重叠约 9 小时，基本正常作息
用工类型Full Time
发布时间今天
数据来源Himalayas
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注意地域限制：该职位明确限定在 United States 招聘。如果你是位于中国大陆的求职者，通常需要当地工作身份才能投递，或需与雇主确认是否接受独立合同（Contractor）形式合作。

类别：IT 服务

地点：

我们正在寻找一名机器学习/AI工程师，负责设计、开发和部署解决实际问题的AI系统。理想的候选人具备扎实的机器学习基础，同时拥有丰富的生产环境实践经验，强大的工程能力，并能在不断变化的环境中独立工作。

您的职责

  • 为实际应用设计、开发和部署机器学习和AI系统。
  • 构建和优化特定领域的自定义AI模型。
  • 设计和维护数据挖掘、数据预处理和数据标注流程。
  • 处理大型数据集，包括特征工程和数据准备。
  • 在诸如大语言模型（LLM）、自然语言处理（NLP）、计算机视觉或其他AI领域中应用机器学习技术。
  • 训练、评估、优化和改进机器学习模型。
  • 开发和维护模型部署和MLOps流程。
  • 使用云平台和容器化环境部署和提供模型。
  • 在协作开发流程中使用Docker、Git和相关MLOps平台等工具。
  • 分析模型性能并识别改进机会。
  • 向非技术利益相关者传达技术概念和项目进展。

要求

  • 2–5年在生产环境中构建和部署ML/AI模型的实际经验。
  • 精通Python。
  • 具有PyTorch和/或TensorFlow的实际经验。
  • 具有设计和构建自定义AI模型的经验。
  • 具有大语言模型（LLM）、自然语言处理（NLP）、计算机视觉或其他AI领域的经验。
  • 对监督和无监督学习、深度学习架构、优化和评估指标有深入理解。
  • 具有数据预处理、特征工程、数据挖掘和数据标注流程的经验。
  • 熟悉MLOps工具和模型部署平台，如MLflow、Kubeflow或SageMaker。
  • 了解用于模型训练和部署的云平台和服务，如AWS Lambda。
  • 具有Docker和容器化模型部署的经验。
  • 熟悉Git和协作软件开发实践。
  • 强大的分析和解决问题的能力。
  • 能够向非技术利益相关者解释复杂的概念。
  • 具有求知欲，能够适应不确定性。
查看英文原文

Category: IT Services

Location:

We are looking for a Machine Learning / AI Engineer to design, develop, and deploy AI systems that solve real-world problems at scale. The ideal candidate combines strong machine learning fundamentals with hands-on production experience, strong engineering skills, and the ability to work independently in an evolving environment.

Your Duties

  • Design, develop, and deploy machine learning and AI systems for real-world applications.
  • Build and optimize custom AI models for domain-specific tasks.
  • Design and maintain data mining, data preprocessing, and data-labeling pipelines.
  • Work with large datasets, including feature engineering and data preparation.
  • Apply machine learning techniques across areas such as LLMs, NLP, computer vision, or other AI domains.
  • Train, evaluate, optimize, and improve machine learning models.
  • Develop and maintain model deployment and MLOps pipelines.
  • Deploy and serve models using cloud platforms and containerized environments.
  • Use tools such as Docker, Git, and relevant MLOps platforms in collaborative development workflows.
  • Analyze model performance and identify opportunities for improvement.
  • Communicate technical concepts and project progress to non-technical stakeholders.

Requirements

  • 2–5 years of hands-on experience building and deploying ML/AI models in production environments.
  • Strong proficiency in Python.
  • Practical experience with PyTorch and/or TensorFlow.
  • Experience designing and architecting custom AI models.
  • Experience with LLMs, NLP, computer vision, or other AI domains.
  • Strong understanding of supervised and unsupervised learning, deep learning architectures, optimization, and evaluation metrics.
  • Experience with data preprocessing, feature engineering, data mining, and data-labeling pipelines.
  • Familiarity with MLOps tools and model deployment platforms such as MLflow, Kubeflow, or SageMaker.
  • Knowledge of cloud platforms and services used for model training and serving, such as AWS Lambda.
  • Experience with Docker and containerized model deployment.
  • Familiarity with Git and collaborative software development practices.
  • Strong analytical and problem-solving skills.
  • Ability to communicate complex technical concepts to non-technical stakeholders.
  • Curiosity-driven mindset and comfort working with ambiguity.

DetailsOriginally posted on Himalayas

本页面信息整理自 Himalayas，版权归原发布方所有。职位可能随时关闭，投递请以原始页面为准。 本站只做信息聚合展示，不参与招聘流程，也不向求职者收取任何费用。

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