Machine Learning Engineer

Summary

Gravity Research is focused on developing AI systems applied in renewable energy and infrastructure environments. The Machine Learning Engineer will contribute to transforming data into forecasting, optimization, and decision-making systems while ensuring deployment readiness and integration into operational workflows.

Responsibilities

• Design, develop, and optimize machine learning models for forecasting, optimization, and classification tasks
• Build and maintain data pipelines for ingesting, processing, and validating data from multiple sources
• Deploy models into production environments, ensuring scalability, reliability, and performance
• Collaborate with engineering teams on system architecture and API integration
• Validate model performance using real-world datasets and operational metrics
• Contribute to system documentation, testing frameworks, and continuous improvement processes
• Support pilot deployments and monitor system behavior in operational environments

Skills

• Strong experience with Python and machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
• Experience working with real-world data pipelines and data processing systems
• Understanding of model deployment, APIs, and cloud-based infrastructure
• Familiarity with time-series data, forecasting, or optimization problems
• Ability to work across research and engineering boundaries
• Strong problem-solving skills and attention to detail

Qualifications

Must Haves

• Strong experience with Python and machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
• Experience working with real-world data pipelines and data processing systems
• Understanding of model deployment, APIs, and cloud-based infrastructure
• Familiarity with time-series data, forecasting, or optimization problems
• Ability to work across research and engineering boundaries
• Strong problem-solving skills and attention to detail