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

24-Mag•New York, NY

20h•Remote

About The Position

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.

Requirements

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
Strong written documentation and technical communication skills
Ability to work effectively within a remote and evolving project environment

Nice To Haves

Background deploying or operationalising machine learning models in cloud or enterprise environments is advantageous
Experience developing or integrating data pipelines for machine learning workflows

Responsibilities

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
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
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
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