Remote ML Engineer — Build Real-World AI Models

Parailabs; Colombia; Remote; COP 263,227,000 - 526,455,000; Part time

Job summary

Parailabs is seeking a Machine Learning Engineer on a remote contract to design and refine ML models using Python and popular frameworks. You will analyze large datasets and manage data with MongoDB, collaborating with cross-functional teams to iteratively improve model performance.

You will evaluate models, tune hyperparameters, and document experiments to ensure repeatable workflows while contributing to data pipeline integration and insightful ML-driven recommendations.

Qualifications

• Proficient in Python and ML frameworks such as scikit-learn, TensorFlow or PyTorch.
• Experience with MongoDB for data manipulation in ML projects.
• Strong problem-solving skills and ability to deliver ML solutions in real-world settings.
• Knowledge of model evaluation metrics, feature engineering, and data preprocessing techniques.

Responsibilities

• Design, develop, and refine machine learning models using Python and libraries to meet objectives.
• Analyze large datasets and leverage MongoDB to manage data for training and validation.
• Collaborate with cross-functional teams to identify improvement areas and implement robust solutions.
• Conduct model evaluation, tune hyperparameters, and benchmark results for optimal performance.
• Document methodologies, experiments, and outcomes for transparent workflows.
• Integrate data pipelines and preprocessing workflows to streamline training and inference.
• Deliver actionable insights and recommendations based on ML outcomes.

Skills

Python

scikit-learn

TensorFlow

PyTorch

Model evaluation

Feature engineering

Data preprocessing

Remote collaboration

Tools

MongoDB

Job description

Parailabs is seeking a Machine Learning Engineer on a remote contract to design and refine ML models using Python and popular frameworks. You will analyze large datasets and manage data with MongoDB, collaborating with cross-functional teams to iteratively improve model performance.

You will evaluate models, tune hyperparameters, and document experiments to ensure repeatable workflows while contributing to data pipeline integration and insightful ML-driven recommendations.