ML Summer Intern

Pravāh • San Francisco, CA

20h

About The Position

Pravah is building foundational intelligence for the electric grid by applying modern machine learning to complex physical infrastructure problems spanning grid operations, weather, and geospatial systems. Their work sits at the intersection of computer vision, physical systems, and large-scale ML, with deployments across utilities in the United States and India. They leverage multimodal data, including satellite imagery, LiDAR, and street-level data, to build high-fidelity representations of grid assets and their surroundings. Pravah is backed by Khosla Ventures, Pear VC, and Conviction. As an ML Intern, you will be a highly motivated student with strong foundations in mathematics, machine learning, and computational methods, working on real, open-ended technical problems at the frontier of AI and physical systems.

Requirements

• Currently pursuing a degree in Computer Science, Electrical Engineering, Applied Math, Physics, or a related field
• Strong foundation in machine learning
• Comfortable with Python and ML frameworks (e.g., PyTorch, TensorFlow, JAX)
• Curious, self-driven, and comfortable working on open-ended problems

Nice To Haves

• Experience with Time series forecasting
• Experience with Computer vision
• Experience with Graph neural networks
• Experience with Geospatial data or physical systems modeling

Responsibilities

• Develop and improve forecasting models for weather and electricity demand
• Work with large-scale weather foundation models, applying geo-targeted corrections, and fine-tuning for regional accuracy
• Train models for improved performance across diverse and challenging real-world conditions
• Build models for object detection, segmentation, and depth estimation
• Apply computer vision techniques to street view, LiDAR, and satellite imagery
• Contribute to world models of physical infrastructure
• Work with Graph neural networks and graph transformers for modeling power flow and temporal dynamics in large-scale grids
• Model and forecast behavior in large-scale power networks
• Work on power flow modeling and system optimization

Benefits

• Hands-on experience solving real-world ML problems with direct impact
• Exposure to cutting-edge research and production systems
• Close collaboration with a deeply technical founding team
• Opportunity to contribute to systems deployed across global energy infrastructure

What This Job Offers

• Job Type: Full-time
• Career Level: Intern