Remote | Applied Machine Learning Evaluation Consultant — Up to

00/hour

About the Role

This role is ideal for experienced Machine Learning Engineers and Applied ML Researchers who have a strong background in developing, training, evaluating, and optimizing machine learning models. You will work independently on open-ended problems, delivering high-quality technical outputs and providing clear written technical feedback to improve correctness, rigor, and reproducibility.

What You Will Do

• 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
• 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

What We Are Looking For

• 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 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 specific metrics or constraints

Benefits and Perks

• Competitive hourly rate of up to
  00/hour
• Opportunity to work on complex machine learning challenges and contribute to high-quality project execution
• Flexible, part-time consulting schedule
• Opportunity to work with a team of experienced machine learning professionals
• Access to industry-standard machine learning tools and frameworks
• Professional development opportunities through training and mentorship
• Remote work stipend and benefits package

• To stand out in your application, make sure to highlight your experience with end-to-end machine learning solution development and model evaluation.
• Showcase your proficiency in Python and modern machine learning frameworks, and be prepared to provide examples of your work.
• Develop a strong portfolio that demonstrates your ability to work independently on open-ended machine learning problems and deliver high-quality technical outputs.
• Be prepared to discuss your approach to model validation and evaluation, and how you stay up-to-date with industry developments and advancements.
• When negotiating salary, be sure to highlight your relevant experience and skills, and be prepared to provide examples of your work and achievements.
• Pay close attention to the company culture and values, and be sure to ask questions about the team and the work environment during the interview process.
• Be wary of red flags such as unclear expectations, lack of communication, or unrealistic deadlines, and be prepared to ask questions and seek clarification during the interview process.