ML Summer Intern

Location San Francisco Employment Type Full time Location Type On-site Department ML Deadline to Apply May 30, 2026 at 12:00 PM EDT Compensation Summer Compensation $5K-$10K About Pravah Pravah is building foundational intelligence for the electric grid. We apply modern machine learning to complex physical infrastructure problems spanning grid operations, weather, and geospatial systems. Our work sits at the intersection of computer vision, physical systems, and large-scale ML, with deployments across utilities in the United States and India. We leverage multimodal data - including satellite imagery, LiDAR, and street-level data - to build high-fidelity representations of grid assets and their surroundings. To know more about who we are, what we are building, and why we are excited read this Notion! Role Overview We are seeking highly motivated students with strong foundations in mathematics, machine learning, and computational methods to join us for a summer internship. As an ML Intern at Pravah, you will work on real, open-ended technical problems at the frontier of AI and physical systems. What You Might Work On Weather & Load Forecasting Develop and improve forecasting models for weather and electricity demand Working 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 Computer Vision for Grid Mapping 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 Graph Machine Learning for Power Systems 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 Who You Are 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) Experience with one or more of the following is a plus: Time series forecasting Computer vision Graph neural networks Geospatial data or physical systems modeling Curious, self-driven, and comfortable working on open-ended problems What You'll Gain 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 Compensation $5,000 - $10,000 per month, depending on experience and background. Timeline & Application Interviews are currently ongoing! So apply as soon as you can #J-18808-Ljbffr