Machine Learning Engineer - AI/Predictive Analytics

Machine Learning Engineer - AI / Predictive Analytics

We're looking for a Machine Learning Engineer to join a growing AI/ML team working on predictive solutions for Comcast's construction organization.

The team is solving several interesting problems with machine learning-from predicting construction timelines and permitting delays to forecasting project costs and exploring automated construction design.

This is a hands-on role for someone who enjoys data, feature engineering, predictive modeling, experimentation, and production ML.

What You'll Be Working On

Predicting Construction Timelines
Improve an existing model that predicts how long construction jobs will take.

Predicting Permit Timelines
Build a model to estimate how long government permitting may take based on historical patterns and other predictive features.

Predicting Construction Costs
Use four years of historical labor and material expense data to predict the expected cost of future construction jobs.

Automating Construction Design
Explore machine learning approaches to help determine things such as where trenches should go, how many poles may be needed, and where cables should be placed.

Responsibilities

• Build, deploy, and improve machine learning models.
• Perform feature engineering on historical and operational data.
• Investigate model accuracy and identify opportunities for improvement.
• Build pipelines for recurring model retraining.
• Develop features that capture historical trends and changing behavior over time.
• Experiment with different predictive modeling approaches.
• Evaluate model performance and investigate errors.
• Work with production ML systems and data pipelines.
• Collaborate with other ML and engineering team members.
• Help move new ML concepts from experimentation into production.

Required / Desired Experience

• Machine Learning Engineering experience.
• Predictive modeling experience.
• Strong Python.
• Experience with XGBoost, LightGBM, or other boosting algorithms .
• Feature engineering.
• Model evaluation.
• Data pipeline development.
• Production ML experience.
• AWS.
• MLflow or comparable experiment tracking.
• Strong analytical and problem-solving skills.

Technology Stack

Python | AWS | XGBoost | MLflow | DVC | FastAPI | HashiCorp Nomad