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AI/ML Engineer for Manufacturing Large Model (Downstream)

Shanghai, China

 

Department：Avatar

职位：制造大模型算法工程师（Downstream）

职责描述：

  1. 与资深制造大模型算法工程师合作，开发基于多模态电池基础大模型的下游应用。
  2. 参与开发和优化多个具体应用模型，包括电芯质量评估模型、生产过程优化模型、电芯设计优化模型、电芯性能预测模型以及电芯设计生成式模型。
  3. 协助实施和优化数据处理管道，确保数据准确性和一致性，以支持下游应用开发。
  4. 应用机器学习和深度学习技术进行模型训练、验证和优化。
  5. 分析和处理复杂的数据集，提取关键特征以改进模型性能和应用效果。
  6. 协助跨职能团队理解业务需求并制定相应的技术实现方案。
  7. 持续跟踪最新技术发展，协助引入前沿方法提升模型应用效果。

任职资格：

  1. 计算机科学、电子工程、统计学或相关领域的硕士学位。
  2. 至少3年以上在机器学习或AI领域的实际工作经验，有制造业或电池生产相关经验者优先。
  3. 熟悉机器学习和深度学习算法，具备多模态数据处理与应用开发经验者优先。
  4. 熟练掌握Python、TensorFlow、PyTorch等常用的机器学习框架和工具。
  5. 具备处理表格数据、时序数据和图像数据的能力，并能够将这些数据应用于具体的模型开发。
  6. 良好的沟通能力和团队协作精神，能有效与跨职能团队合作。
  7. 具备良好的问题解决能力和学习新技术的意愿。

 

Responsibilities:

  1. Cooperate with senior AI/ML engineers to develop downstream applications based on multi-mode battery basic large model.
  2. Participate in the development and optimization of multiple specific application models, including cell quality assessment model, production process optimization model, cell design optimization model, cell performance prediction model and cell design generative model.
  3. Assist in implementing and optimizing data processing pipelines to ensure data accuracy and consistency to support downstream application development.
  4. Apply machine learning and deep learning technology for model training, verification and optimization.
  5. Analyze and process complex data sets, extract key features to improve model performance and application effect.
  6. Assist cross-functional teams to understand business requirements and develop corresponding technical implementation plans.
  7. Continue to track the latest technological development and help introduce cutting-edge methods to improve the application effect of the model.

Qualifications:

  1. Master's degree in computer science, electrical engineering, statistics or related field.
  2. At least 3 years of practical working experience in machine learning or AI field, manufacturing or battery production experience is preferred.
  3. Familiar with machine learning and deep learning algorithms, multi-modal data processing and application development experience is preferred.
  4. Proficient in Python, TensorFlow, PyTorch and other commonly used machine learning frameworks and tools.
  5. Have the ability to process tabular data, time series data and image data, and can apply these data to specific model development.
  6. Good communication skills and team spirit, able to effectively work with cross-functional teams.
  7. Good problem-solving skills and willingness to learn new technologies.

 

 

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