Remote | Applied Machine Learning Evaluation Consultant — Up to $100/hour

24-Mag•New York, NY

7h•Remote

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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
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
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