Machine Learning Engineer II, Quản lý chi tiêu

Job PurposeTake ownership of the end-to-end machine learning lifecycle for tabular data, from research, dataset preparation, feature engineering, and model training to high-performance deployment and production monitoring across domains such as transaction categorization, recurring transaction prediction. Bridge the gap between AI research and production engineering by designing rigorous experimental frameworks, optimizing model inference latency, and ensuring continuous model reliability. Mô tả công việcImplement Machine Learning algorithms (XGBoost, LightGBM, CatBoost, Neural Networks) to enhance model performance for existing products . Be thoroughly familiar with modeling lifecycle from data gathering, feature engineering, and cleansing to model training, validation, monitoring and error analysis to ensure model robustness and generalization. Design scalable feature extraction pipelines, handle complex data quality issues, missing values, high-cardinality categorical variables, and severe class imbalance (SMOTE, Focal Loss, class weighting). Collaborate with engineering and product teams to integrate model API endpoints into core products. Review and reproduce recent research (tabular machine learning / deep learning, time-series forecasting, and interpretability (SHAP / LIME)), propose modeling improvements, and communicate findings through technical reports and presentations. Yêu cầu công việcBachelor’s degree in Computer Science, Engineering, Mathematics, Data Science, or a related field.3+ years of industry experience in software engineering/machine learning;Strong Python skills and hands-on experience with tabular processing libraries (Pandas, NumPy, Scikit-Learn, PySpark/Polars) and GBDT frameworks (XGBoost, LightGBM, CatBoost). In-depth understanding of the complete modeling lifecycle: data gathering, feature engineering, hyperparameter tuning, model serving, and post-deployment monitoring. Proficiency in PyTorch or TensorFlow for building custom neural architectures and tabular deep learning models. Ability to read, reproduce, and extend recent research papers. Strong analytical, problem-solving, and cross-functional collaboration skills.