Machine Learning Engineer Expert

Required Qualifications

• Develop end-to-end machine learning solutions for challenging prediction and modeling problems
• Analyze datasets and define appropriate modeling approaches, validation strategies, and evaluation metrics
• Perform exploratory data analysis, feature engineering, and data preprocessing
• Train, tune, and evaluate machine learning models across tabular, text, image, and time-series datasets
• Develop strong reference solutions using industry-standard machine learning techniques and best practices
• Review and validate the technical quality of machine learning projects and deliverables
• Document methodologies, assumptions, and evaluation results in a clear and reproducible manner
• Identify opportunities to improve model performance through systematic experimentation and iteration
• Maintain a Master's degree or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Electrical Engineering, or a related field from a top-tier university
• Have 2+ years of hands-on experience developing, training, evaluating, and optimizing machine learning models in a professional or research setting
• Have strong proficiency in Python and modern machine learning frameworks (e.g., scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow)
• Have demonstrated experience building end-to-end machine learning solutions, including data preparation, model development, validation, and evaluation
• Have a strong understanding of model evaluation metrics, validation methodologies, and experimental design
• Be able to work independently on open-ended machine learning problems and deliver high-quality technical outputs
• Have experience with one or more of the following areas: Tabular machine learning, Natural language processing, Computer vision, Recommendation systems, Ranking systems, Time-series forecasting

Preferred Qualifications

• Hold a PhD from a leading research university
• Have experience at leading technology companies, AI labs, research institutions, or high-growth startups
• Have participation in competitive machine learning or data science competitions
• Have experience optimizing models against performance-based evaluation metrics
• Have familiarity with advanced techniques such as ensembling, hyperparameter optimization, transfer learning, foundation model fine-tuning, or reinforcement learning
• Have publications, patents, or significant open-source contributions in machine learning or AI
• Have experience reviewing, mentoring, or evaluating the work of other machine learning practitioners