Overview

Machine Learning Engineer

Responsibilities

• Design, develop, and refine machine learning models using Python and relevant ML frameworks.
• Analyze large datasets and utilize MongoDB for efficient data storage, retrieval, and management.
• Build and optimize data pipelines, preprocessing workflows, and feature engineering processes.
• Evaluate model performance using appropriate metrics, benchmarking techniques, and validation methodologies.
• Conduct hyperparameter tuning and experimentation to improve model accuracy and efficiency.
• Collaborate with project contributors to identify opportunities for model enhancement and implementation.
• Document methodologies, experiments, results, and technical decisions to ensure reproducibility.
• Deliver actionable insights and recommendations based on machine learning outcomes and data analysis.
• Support workflow improvements across training, evaluation, and inference processes.

Qualifications

• Strong proficiency in Python.
• Strong understanding of machine learning concepts, model evaluation, and statistical analysis.
• Knowledge of feature engineering, model validation, and performance optimization techniques.
• Strong analytical and problem-solving skills.
• Excellent written communication and technical documentation abilities.
• Ability to work independently in a remote environment.
• Familiarity with cloud platforms, MLOps workflows, and scalable ML infrastructure.
• Strong understanding of experimentation, benchmarking, and model performance analysis.
• Ability to adapt quickly to evolving project requirements and collaborate effectively in remote teams.
• $80–$140/hour
• Remote contract engagement
• Flexible schedule
• Project-based work
• Fully remote opportunity.
• 35 openings available.
• Flexible workload with no fixed schedule.
• Applications reviewed on a rolling basis.
• No prior AI experience required.
• Work involves real-world machine learning development, evaluation, experimentation, and optimization tasks.

Nice to have

• Deep familiarity with machine learning frameworks such as Scikit-learn, TensorFlow, or PyTorch.
• Hands-on experience with MongoDB for data manipulation, storage, and retrieval.