Machine Learning Engineer

Key Responsibilities

• Data Preparation: Collect, preprocess, and clean data to make it suitable for machine learning tasks. Work closely with data engineers to access and transform data as needed.
• Model Development: Design, implement, and optimize machine learning models, including traditional techniques such as autoregressive (AR), Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), Exponential Smoothing (ES), as well as advanced algorithms like XGBoost, Prophet, LSTM (Deep Learning), DeepAR, N-BEATS, and Temporal Fusion Transformer (Google), to solve prioritization and classification problems effectively.
• Feature Engineering: Conduct comprehensive feature engineering to enhance model performance and interpretability. Leverage domain knowledge and innovative techniques to create informative features.
• Model Training and Evaluation: Train and evaluate machine learning models using rigorous methodologies and performance metrics. Fine-tune model parameters and hyperparameters to achieve optimal results.
• Hyperparameter Tuning: Optimize model hyperparameters to improve performance and adapt models to evolving data patterns.
• Deployment: Deploy machine learning models into production environments, ensuring they integrate seamlessly with existing systems and are scalable.
• Model Monitoring: Establish monitoring systems to track model performance over time and address any issues that may arise.
• Collaboration: Collaborate with cross-functional teams, including data scientists, data engineers, software engineers, and business stakeholders, to understand their requirements and provide effective solutions.
• Documentation: Document the entire machine learning pipeline, model specifications, and results for clear communication and knowledge sharing.
  

Requirements

• Bachelor's degree or higher in computer science, machine learning, data science, or a related field.
• Solid understanding of statistical methods and machine learning algorithms.
• Proven experience as a Machine Learning Engineer with a focus on ranking, prioritization, classification, and clustering tasks.
• Strong experience in working with labeled data to build machine-learning models.
• Proficiency in using machine learning libraries such as scikit-learn, TensorFlow, or PyTorch.
• Experience with data preprocessing, feature engineering, and data visualization.
• Strong programming skills in Python.
• 5+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).
• Proven track record of developing and deploying live production systems as part of a product team for at least 3 years.
• Experience leading the development and deployment of products or systems throughout the product cycle from ideation to shipping.
• Expertise in traditional machine learning techniques (AR, ARIMA, SARIMA, ES), deep learning architectures (LSTM, DeepAR, N-BEATS, Temporal Fusion Transformer), and ensemble methods (XGBoost).
• Deep understanding of machine learning algorithms and model evaluation techniques.
• Knowledge of deep learning techniques and neural networks is a plus.
• Experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization (e.g., Docker).
• Strong problem-solving and analytical skills.
• Excellent communication and teamwork skills.
• Experience with natural language processing (NLP) and text classification is a bonus.
• Knowledge of reinforcement learning or recommendation systems is a plus.