Machine Learning Engineering Expert (Remote | $90/hr)
Machine Learning Engineer Expert
Location: Remote (U.S. / UK / Canada / Europe / Australia)
Engagement Type: Hourly Contract
Compensation: $90/hour
Commitment: 10+ hours per week
About the Opportunity
This opportunity is for experienced Machine Learning Engineers and Applied ML Researchers interested in contributing to advanced AI research and evaluation projects. The role focuses on developing, solving, and evaluating complex machine learning challenges that mirror real-world ML workflows while helping improve the capabilities of next-generation AI systems.
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.
• Build high-quality 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.
• Improve model performance through systematic experimentation, optimization, and iteration.
Required Qualifications
• 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 environment.
• Strong proficiency in Python and modern machine learning frameworks such as:
• 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 in one or more of the following areas:
• Tabular Machine Learning
• Natural Language Processing (NLP)
• Computer Vision
• Recommendation Systems
• Ranking Systems
• Time-Series Forecasting
• Ability to work independently and deliver high-quality technical solutions on open-ended machine learning problems.
Preferred Qualifications
• PhD from a leading research university.
• Experience at leading technology companies, AI labs, research institutions, or high-growth startups.
• Participation in machine learning, AI, or data science competitions.
• Experience optimizing models against performance-based evaluation metrics.
• Familiarity with advanced machine learning techniques, including:
• Hyperparameter Optimization
• Ensembling
• Transfer Learning
• Foundation Model Fine-Tuning
• Reinforcement Learning
• Publications, patents, or significant open-source contributions in Machine Learning or Artificial Intelligence.
• Experience reviewing, mentoring, or evaluating the work of other machine learning practitioners.
Compensation
• Competitive compensation of $90/hour.
• Weekly payments.
• Independent contractor engagement.
Application Process
• Upload Resume
• Complete an AI Interview Based on Your Resume
• Submit Application
Location: Remote (U.S. / UK / Canada / Europe / Australia)
Engagement Type: Hourly Contract
Compensation: $90/hour
Commitment: 10+ hours per week
About the Opportunity
This opportunity is for experienced Machine Learning Engineers and Applied ML Researchers interested in contributing to advanced AI research and evaluation projects. The role focuses on developing, solving, and evaluating complex machine learning challenges that mirror real-world ML workflows while helping improve the capabilities of next-generation AI systems.
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.
• Build high-quality 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.
• Improve model performance through systematic experimentation, optimization, and iteration.
Required Qualifications
• 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 environment.
• Strong proficiency in Python and modern machine learning frameworks such as:
• 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 in one or more of the following areas:
• Tabular Machine Learning
• Natural Language Processing (NLP)
• Computer Vision
• Recommendation Systems
• Ranking Systems
• Time-Series Forecasting
• Ability to work independently and deliver high-quality technical solutions on open-ended machine learning problems.
Preferred Qualifications
• PhD from a leading research university.
• Experience at leading technology companies, AI labs, research institutions, or high-growth startups.
• Participation in machine learning, AI, or data science competitions.
• Experience optimizing models against performance-based evaluation metrics.
• Familiarity with advanced machine learning techniques, including:
• Hyperparameter Optimization
• Ensembling
• Transfer Learning
• Foundation Model Fine-Tuning
• Reinforcement Learning
• Publications, patents, or significant open-source contributions in Machine Learning or Artificial Intelligence.
• Experience reviewing, mentoring, or evaluating the work of other machine learning practitioners.
Compensation
• Competitive compensation of $90/hour.
• Weekly payments.
• Independent contractor engagement.
Application Process
• Upload Resume
• Complete an AI Interview Based on Your Resume
• Submit Application