Machine Learning Engineer

Remotebridge; United States; Remote; USD 90,000 - 130,000; Part time

Job summary

Remotebridge is seeking a Machine Learning Engineer to contribute to a dynamic customer project. You will design, train, and refine ML models using Python and leading ML frameworks while working remotely in a cross-functional team.

You will analyze large datasets with MongoDB, perform model evaluation and hyperparameter tuning, and document experiments to ensure reproducibility. Strong communication and ability to adapt to evolving project requirements are essential.

Qualifications

• Proficient in Python with deep familiarity of ML frameworks (scikit-learn, TensorFlow, PyTorch).
• Experience with data preprocessing and feature engineering.
• Experience deploying ML models in cloud/enterprise environments.

Responsibilities

• Design, develop, and refine ML models using Python and relevant libraries.
• Analyze large datasets and leverage MongoDB to manage data for training and validation.
• Collaborate with cross-functional teams to identify model improvement areas and implement robust solutions.
• Conduct model evaluation, tune hyperparameters, and benchmark results for performance.
• Document methodologies, experiments, and outcomes for repeatable workflows.
• Integrate data pipelines and preprocessing workflows to streamline training and inference.

Skills

Python

ML frameworks

Model training

Tools

MongoDB

Job description

Role Title: Machine Learning Engineer

Role Type: Contractor

Location: Remote

Our team is engaging Machine Learning Engineers to contribute expertise to a dynamic customer project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input.

Scope of Work

• Design, develop, and refine machine learning models using Python and relevant libraries to address project objectives.
• Analyze large datasets and leverage MongoDB to manage and retrieve data efficiently for model training and validation.
• Collaborate with cross-functional contributors to identify areas for model improvement and implement robust solutions.
• Conduct thorough model evaluation, tuning hyperparameters, and benchmarking results to ensure optimal performance.
• Document methodologies, experiments, and outcomes to ensure transparent and repeatable workflows.
• Integrate data pipelines and preprocessing workflows to streamline training and inference processes.
• Deliver actionable insights and recommendations based on data-driven findings and machine learning outcomes.

Preferred Qualifications

• Demonstrated expertise with Python, including deep familiarity with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch.
• Hands-a… but we can't see rest.
• Strong problem-solving skills and a track record of delivering innovative ML solutions in real-world settings.
• Understanding of model evaluation metrics, feature engineering, and effective data preprocessing techniques.
• Background in deploying or operationalizing machine learning models in cloud or enterprise environments.
• Clear written documentation and communication skills for sharing technical findings and best practices.
• Ability to adapt quickly to evolving project requirements and contribute collaboratively in a remote setting.