Machine Learning Engineer | Remote

Crossing Hurdles; India; Remote; INR 10,476,000 - 18,333,000; Part time

Job summary

Crossing Hurdles is seeking a Machine Learning Engineer for a remote contract role with 10-40 hours per week. Design, train, and refine ML models using Python and ML libraries, and work with large datasets in a flexible, async environment.

Ideal candidates have deep experience with Python ML frameworks (scikit-learn, TensorFlow, PyTorch) and MongoDB, strong problem-solving, and the ability to document methods and results for repeatable workflows. Diversity of projects is expected.

Qualifications

• Demonstrated expertise with Python and ML frameworks (scikit-learn, TensorFlow, PyTorch).
• Hands-on experience with MongoDB for data manipulation in ML projects.
• Strong problem-solving and ability to deliver innovative ML solutions.

Responsibilities

• Design, develop, and refine ML models using Python and libraries to meet project objectives.
• Analyze large datasets and leverage MongoDB for data management and retrieval.
• Collaborate with cross-functional teams to identify model improvements.
• Conduct model evaluation, tune hyperparameters, and benchmark performance.
• Document methodologies and results for transparent workflows.
• Integrate data pipelines and preprocessing to streamline training and inference.
• Deliver data-driven insights and recommendations from ML outcomes.

Skills

Python

TensorFlow

PyTorch

Scikit-learn

MongoDB

Data preprocessing

Model evaluation

Cloud deployment

Documentation

Communication

Job description

• 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.

Position:

Machine Learning Engineer

Type:

Contract

Compensation:

$80 - $140/hour

Location:

Remote

Commitment:

10-40 hrs/week

Role Responsibilities

• 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.

Requirements

• Demonstrated expertise with Python, including deep familiarity with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch.
• Hands-on experience with MongoDB for data manipulation, storage, and retrieval within machine learning projects.
• 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.

Application Process

• Easy Apply on LinkedIn
• Check email for next steps
• Participate in resume evaluation & interview stage