Responsibilities
- Build and benchmark next-generation multiscale, regional, and global forecasting systems against reanalysis and observations, with particular focus on nowcasting and extreme events.
- Run cycling DA–forecast loops end to end lateral boundary conditions, SSTs, soil states, and spin-up at convection-permitting (~1 km) resolution over Indian sub-regions.
- Stand up rigorous forecast verification across deterministic (RMSE, bias, spectra) and probabilistic (CRPS, BSS) metrics.
- Tailor weather prediction models to renewable-sector needs, particularly solar (GHI) and wind generation (100m winds).
- Assist in training AI-based weather prediction models.
- Work at the intersection of physics-based modeling and machine learning hybrid physics–ML systems, learned parameterizations, and emulators.
Requirements
- A master's or PhD in geophysical sciences, physics, applied mathematics, computer science, statistics, or a related field. A bachelor's degree with 3+ years of relevant research or operational experience is also acceptable.
- Demonstrated depth in numerical weather prediction, evidenced by operational work, model contributions, research projects, publications, or technical reports.
- Hands-on work with limited-area or mesoscale models such as WRF, MPAS, or comparable systems including dynamical cores, physics parameterizations, and boundary-layer/convection schemes configuring and running them end to end (domains, lateral boundaries, physics suites, spin-up and stability), tuning parameterizations, diagnosing systematic biases, and verifying against observations or reanalysis.
- Experience running convection-resolving simulations at high spatial resolution (~1 km).
- Familiarity with existing operational forecasting models (IFS, GFS, BharatFS).
- Experience contributing to or maintaining model code, or holding responsibility in an operational or quasi-operational forecasting pipeline.
- Experience working with TB-scale, high-dimensional observational and modeling datasets (reanalysis, satellite, radar, weather-station, and sounding data) and the geospatial pipework (grids, reprojection, masks) around them.
- Hands-on experience with widely used reference datasets such as ERA5, MERRA-2, IMDAA, IMERG/GPM, and GOES/INSAT/Himawari.
- Practical experience on High Performance Computers (HPCs).
- Fluency in the modern geoscience Python stack: xarray, dask, zarr, netCDF.
- Experience building reproducible, production-grade pipelines.
- Excellent written and verbal communication, including the ability to explain technical work to both domain experts and cross-disciplinary collaborators.
Nice to Have
- Prior work on projects specific to Indian geography.
- Familiarity with coupled earth-system models.
- Experience with any of: ensemble and probabilistic forecasting, regional downscaling, or subseasonal-to-seasonal (S2S) prediction.
- Experience working with operational forecasting agencies (IMD, NCMRWF, ECMWF, NOAA, etc.).
- Familiarity with AI-based weather prediction models and data assimilation techniques.
- Comfort using agentic AI tools to accelerate development.
- Publications in respected atmospheric, oceanic, or climate science venues.
Benefits
- Part of development of weather forecasting models deployed for real-time applications.
- Experience working on hard, open-ended problems at the intersection of AI and physical infrastructure.
- Exposure to how teams set priorities and push the frontier of AI weather prediction.
- Close collaboration with a deeply technical team.
Work Arrangement
Remote (Worldwide) — India, United States
Additional Information
- Working hours: expect a few hours of evening overlap with US Pacific Time on most workdays.
