We are sharing a specialised part-time consulting opportunity for experienced Machine Learning Engineers and Applied ML Researchers with expertise in end-to-end modeling, dataset analysis, feature engineering, validation strategy, model evaluation, reference solution development, and technical quality review.
This role supports current and upcoming remote consulting opportunities focused on complex machine learning challenge design, applied modeling workflows, reference solution development, technical evaluation, reproducible documentation, and high-quality project execution. Selected professionals will design, solve, and review challenging machine learning tasks that reflect real-world ML development across multiple domains and data modalities.
Key Responsibilities
Professionals in this role may contribute to:
End-to-End Machine Learning Solution Development
Develop complete 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, data preprocessing, model training, tuning, and evaluation
Work across tabular, text, image, time-series, recommendation, ranking, or other applied ML problem types Reference Solutions & Technical Documentation
Develop strong reference solutions using industry-standard machine learning techniques and best practices
Document methodologies, assumptions, modeling choices, validation approaches, and evaluation results clearly
Ensure solutions are accurate, reproducible, and technically well-structured
Identify opportunities to improve model performance through systematic experimentation and iteration ML Project Review & Evaluation
Review and validate the technical quality of machine learning projects and deliverables
Evaluate modeling choices, data preparation decisions, performance metrics, and experimental design
Identify weak assumptions, data leakage risks, flawed validation, underdeveloped features, or unsupported modeling conclusions
Provide clear written technical feedback that improves correctness, rigor, and reproducibility Ideal Profile
Strong candidates may have:
Master's degree, PhD, or equivalent advanced experience in Computer Science, Machine Learning, Statistics, Mathematics, Electrical Engineering, or a related field
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 modern machine learning frameworks such as scikit-learn, XGBoost, LightGBM, PyTorch, or 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
Ability to work independently on open-ended machine learning problems and deliver high-quality technical outputs
Relevant Experience May Include:
Tabular machine learning
Natural language processing
Computer vision
Recommendation systems
Ranking systems
Time-series forecasting
Applied modeling across structured or unstructured datasets Educational Background
Master's degree, PhD, or equivalent advanced technical experience in machine learning, computer science, statistics, mathematics, electrical engineering, data science, or a related field is highly relevant
Academic or research experience from a strong technical program may be especially valuable
Professional machine learning experience, applied research experience, open-source contributions, or competitive ML work may also be relevant depending on project needs Nice to Have
PhD from a leading research university
Experience at leading technology companies, AI-focused teams, 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 Why This Opportunity
Apply machine learning engineering and applied research expertise to structured remote consulting work
Contribute to high-quality ML challenge design, reference solution development, and technical evaluation
Work on flexible assignments aligned with your modeling, Python, experimentation, and ML framework experience
Use your technical judgment to evaluate complex ML workflows and improve solution quality
Remote structure with competitive hourly compensation Contract Details
Independent contractor role
Fully remote with flexible scheduling
Eligible professionals may be based in approved project locations depending on project needs
Project commitment may vary depending on availability and scope
Competitive rates up to $100 per hour depending on expertise and project scope
Weekly payments via Stripe or Wise
Projects may be extended, shortened, or adjusted depending on scope and performance
Work will not involve access to confidential or proprietary information from any employer, client, or institution About the Platform
This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.
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