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

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 ResponsibilitiesProfessionals in this role may contribute to:End-to-End Machine Learning Solution DevelopmentDevelop complete machine learning solutions for challenging prediction and modeling problemsAnalyze datasets and define appropriate modeling approaches, validation strategies, and evaluation metricsPerform exploratory data analysis, feature engineering, data preprocessing, model training, tuning, and evaluationWork across tabular, text, image, time-series, recommendation, ranking, or other applied ML problem typesReference Solutions & Technical DocumentationDevelop strong reference solutions using industry-standard machine learning techniques and best practicesDocument methodologies, assumptions, modeling choices, validation approaches, and evaluation results clearlyEnsure solutions are accurate, reproducible, and technically well-structuredIdentify opportunities to improve model performance through systematic experimentation and iterationML Project Review & EvaluationReview and validate the technical quality of machine learning projects and deliverablesEvaluate modeling choices, data preparation decisions, performance metrics, and experimental designIdentify weak assumptions, data leakage risks, flawed validation, underdeveloped features, or unsupported modeling conclusionsProvide clear written technical feedback that improves correctness, rigor, and reproducibilityIdeal ProfileStrong candidates may have:Master's degree, PhD, or equivalent advanced experience in Computer Science, Machine Learning, Statistics, Mathematics, Electrical Engineering, or a related field2+ years of hands-on experience developing, training, evaluating, and optimizing machine learning models in a professional or research settingStrong proficiency in Python and modern machine learning frameworks such as scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlowDemonstrated experience building end-to-end machine learning solutions, including data preparation, model development, validation, and evaluationStrong understanding of model evaluation metrics, validation methodologies, and experimental designAbility to work independently on open-ended machine learning problems and deliver high-quality technical outputsRelevant Experience May Include:Tabular machine learningNatural language processingComputer visionRecommendation systemsRanking systemsTime-series forecastingApplied modeling across structured or unstructured datasetsEducational BackgroundMaster'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 relevantAcademic or research experience from a strong technical program may be especially valuableProfessional machine learning experience, applied research experience, open-source contributions, or competitive ML work may also be relevant depending on project needsNice to HavePhD from a leading research universityExperience at leading technology companies, AI-focused teams, research institutions, or high-growth startupsParticipation in competitive machine learning or data science competitionsExperience optimizing models against performance-based evaluation metricsFamiliarity with advanced techniques such as ensembling, hyperparameter optimization, transfer learning, foundation model fine-tuning, or reinforcement learningPublications, patents, or significant open-source contributions in machine learning or AIExperience reviewing, mentoring, or evaluating the work of other machine learning practitionersWhy This OpportunityApply machine learning engineering and applied research expertise to structured remote consulting workContribute to high-quality ML challenge design, reference solution development, and technical evaluationWork on flexible assignments aligned with your modeling, Python, experimentation, and ML framework experienceUse your technical judgment to evaluate complex ML workflows and improve solution qualityRemote structure with competitive hourly compensationContract DetailsIndependent contractor roleFully remote with flexible schedulingEligible professionals may be based in approved project locations depending on project needsProject commitment may vary depending on availability and scopeCompetitive rates up to $100 per hour depending on expertise and project scopeWeekly payments via Stripe or WiseProjects may be extended, shortened, or adjusted depending on scope and performanceWork will not involve access to confidential or proprietary information from any employer, client, or institutionAbout the PlatformThis opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.By submitting this application, you acknowledge that your information may be processed by 24-MAG LLC for recruitment and opportunity matching in accordance with our Privacy Policy: https://www.24-mag.com/privacy-policy.Originally posted on Himalayas