About The Job Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark , General Catalyst , Peter Thiel , Adam D'Angelo , Larry Summers , and Jack Dorsey .

Position: Machine Learning Engineer Expert

Type: Contract Compensation: $90/hour Location: Remote Role Responsibilities

• 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.
• Review and validate the technical quality of machine learning projects and deliverables.
• Identify opportunities to improve model performance through systematic experimentation and iteration.

• 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 professional experience in machine learning, applied AI, data science, or a closely related field.
• Strong proficiency in Python and modern machine learning frameworks (e.g., scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow).
• Demonstrated experience building end-to-end machine learning solutions, including data preparation, model development, validation, and evaluation.
• Strong understanding of model evaluation metrics, validation methodologies, and experimental design.
• Experience with one or more of the following areas: tabular machine learning, natural language processing, computer vision, recommendation systems, ranking systems, time-series forecasting.
• Ability to work independently on open-ended machine learning problems and deliver high-quality technical outputs.

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

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