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.
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
