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.
We are backed by Khosla Ventures, Pear VC, and Conviction.
To know more about who we are, what we are building, and why we are excited read this Notion! https://pravah.notion.site/
The role
We are hiring an AI Weather Scientist to advance the next generation of weather forecasting systems. You will work closely with machine learning and software engineers on four core threads:
Run numerical weather prediction models to generate high-resolution forecasts and training data.
Inform the development of AI weather forecasting models and innovate on existing architectures.
Evaluate pre-trained global and regional models against reanalysis, satellite, and ground observations to identify areas for improvement.
Procure, process, and create ML-ready global and regional weather datasets, with explicit focus on data-sparse regions.
Drive the development of next-generation multiscale, regional, and global weather forecasting systems, and their benchmarking against reanalysis and observations, especially during extreme events and over data-sparse regions.
Tailor weather prediction models to sector-specific needs: energy (solar and wind demand/generation, grid stress), agriculture (seasonal outlooks, crop-relevant variables), and extreme-weather resilience (heatwaves, heavy precipitation, tropical and extratropical cyclones, convective storms).
Assess the applicability of state-of-the-art AI methodologies including foundation models, generative architectures, and physics-informed ML to weather and climate forecasting.
Work at the intersection of physics-based modeling and machine learning: hybrid physics–ML approaches, learned parameterizations, and emulators.
Any combination of the following will strengthen your application. We do not expect you to have all of them.
Preferred qualifications*
A master's or PhD in geophysical sciences, physics, applied mathematics, computer science, statistics, or a related field. A bachelor's with 7+ years of relevant research or operational experience is also acceptable.
Demonstrated depth in either numerical weather prediction, meteorology, or earth system modeling through research projects, publications, model contributions, or operational work.
Experience working with high-dimensional observational and modeling datasets (reanalysis products, satellites, weather stations) in forecasting
Experience working with deep learning models and familiarity with at least one framework (PyTorch, JAX, or TensorFlow).*
Excellent written and verbal communication, including the ability to explain technical work to both domain experts and cross-disciplinary collaborators.
Hands-on experience with high-resolution regional earth-system models such as WRF or MPAS, including dynamical cores, physics parameterizations, boundary-layer and convection schemes, or coupled ocean–atmosphere configurations.
Experience with operational forecasting models or workflows (real-time data ingest, verification, cycling, product generation).
Experience with either of data assimilation, ensemble and probabilistic forecasting, convection-permitting or mesoscale modeling, regional downscaling, or subseasonal-to-seasonal (S2S) prediction.
Experience using or building AI weather prediction models - whether benchmarking, fine-tuning, or extending them. Applying generative AI and diffusion models to weather and climate is a strong plus.
Publications in leading atmospheric, oceanic, or climate science venues and/or major ML/AI conferences.
Ownership of weather forecasting models deployed for real-time applications.
Experience working on hard, open-ended problems at the intersection of AI and physical infrastructure.
Ability to shape technical direction and shape the frontier of AI-weather prediction revolution.
Close collaboration with a deeply technical founding team.
This role sits at the frontier of the AI-weather revolution, applying modern machine learning to earth system modeling. The next decade of progress in weather and climate prediction will be built by scientists who understand the physics and the data and have learned to wield generative AI. You will be working in data-sparse regions where data is heterogeneous, ground truth is incomplete, and progress requires both technical depth and first-principles thinking.
What We Do
Pravāh is a Stanford-founded, AI-powered grid intelligence company building foundational intelligence for the electric grid. They develop real-time forecasting and optimization systems to make electricity cleaner, more affordable, and reliable.
