Remote | Machine Learning Engineer — $80–$140/hour

We are sharing a specialised consulting opportunity for experienced Machine Learning Engineers with strong expertise in Python, machine learning frameworks, model evaluation, data pipelines, and MongoDB to contribute to an advanced AI training and machine learning project.

Selected professionals will design and refine machine learning models, work with large datasets, develop efficient preprocessing and data pipelines, and evaluate model performance through rigorous experimentation and benchmarking. The work requires strong practical ML expertise, structured technical reasoning, and the ability to document methodologies and findings clearly.

Key Responsibilities

Machine Learning Model Development

• Design, develop, and refine machine learning models using Python and relevant frameworks
• Select appropriate modelling approaches based on project objectives and available data
• Implement robust training and validation workflows
• Apply feature engineering and data-preparation techniques where appropriate
• Improve model quality through iterative experimentation and analysis

Model Evaluation & Optimisation

• Conduct systematic model evaluation using appropriate performance metrics
• Tune hyperparameters to improve model effectiveness and stability
• Benchmark alternative models, configurations, and approaches
• Identify weaknesses, failure patterns, and opportunities for improvement
• Ensure conclusions are supported by reproducible experimental results

Data Engineering & MongoDB

• Analyse and work with large datasets used for model training and validation
• Use MongoDB for efficient data storage, retrieval, and manipulation
• Develop preprocessing workflows that support reliable model development
• Integrate data pipelines across training and inference processes
• Identify and resolve data-quality or pipeline issues affecting model performance

Experimentation, Documentation & Collaboration

• Document methodologies, experiments, assumptions, and outcomes clearly
• Maintain transparent and repeatable machine learning workflows
• Collaborate with cross-functional contributors to identify model-improvement opportunities
• Communicate technical findings and recommendations in a clear, actionable manner
• Adapt modelling approaches as project requirements evolve

Ideal Profile

• Demonstrated professional expertise in Python
• Strong experience with machine learning frameworks such as scikit-learn, TensorFlow, PyTorch, or comparable tools
• Hands-on experience using MongoDB for data storage, retrieval, and manipulation
• Strong understanding of machine learning fundamentals and model-development workflows
• Familiarity with model evaluation metrics, hyperparameter tuning, and benchmarking
• Practical experience with feature engineering and data preprocessing
• Strong problem-solving ability and experience delivering real-world ML solutions
• Background deploying or operationalising machine learning models in cloud or enterprise environments is advantageous
• Experience developing or integrating data pipelines for machine learning workflows
• Strong written documentation and technical communication skills
• Ability to work effectively within a remote and evolving project environment

Engagement Details

• Independent contractor engagement
• Fully remote
• Compensation: $80–$140/hour
• Work will involve machine learning model development, data analysis, MongoDB, model evaluation, experimentation, and pipeline integration
• Strong practical Python and machine learning experience are central to this engagement
• Experience deploying or operationalising ML systems is highly valued
• Project scope, workload, technical requirements, and evaluation criteria may evolve depending on project needs
• Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party

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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