Responsibilities
• Develop end-to-end machine learning solutions for challenging prediction and modeling problems
• Analyze datasets and choose appropriate modeling approaches, validation strategies, and evaluation metrics
• Perform exploratory data analysis, feature engineering, and preprocessing
• Train, tune, and evaluate models across tabular, text, image, and time-series datasets
• Build strong reference solutions using industry-standard machine learning techniques
• Review and validate the technical quality of machine learning projects and deliverables
• Document methodologies, assumptions, and results clearly and reproducibly
• Identify opportunities to improve performance through systematic experimentation

Requirements
• Master's degree or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Electrical Engineering, or a related field from a top-tier university
• 2+ years of hands-on experience developing, training, evaluating, and optimizing machine learning models in a professional or research setting
• Strong proficiency in Python and frameworks such as scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow
• Demonstrated experience building end-to-end machine learning solutions
• Strong understanding of model evaluation metrics, validation methodologies, and experimental design
• Experience in one or more of the following: tabular ML, NLP, computer vision, recommendation systems, ranking systems, or time-series forecasting
• Ability to work independently on open-ended machine learning problems

How to Apply
Click "Apply" to be taken to the Mercor website. Candidates with PhDs, experience at top technology companies or AI labs, competition backgrounds, advanced optimization skills, or notable research and open-source work may be especially strong fits. Applying through our link supports WFH Bulletin as a referral partner, but you are welcome to apply directly if you prefer.