Meteorology Software Developer

Combine boundary-layer meteorology, numerical weather prediction, and production-grade software development to operationalize the forecasts that drive WireWeather's grid-aware line ratings.

As a Meteorology Software Developer at Pitch, you will leverage your background in atmospheric science (especially boundary-layer meteorology and nowcasting) to develop, refine, and operationalize weather models that produce high-resolution stochastic wind and temperature forecasts along power lines.

Those forecasts feed our Dynamic Line Rating (DLR), Ambient-Adjusted Rating (AAR), and Contingency Line Rating (CLR) products, as well as our wildfire and outage-risk tools.

The role is split roughly evenly between meteorology and development.

You'll integrate physics-based models (WRF, WindNinja, or similar CFD frameworks), national and international NWP outputs (HRRR, GFS, ECMWF, RAP), AI-based forecast systems, and real-time observations from WireWarrior sensors and public networks.

You'll also build the Python pipelines, and APIs that turn that work into a product utilities can rely on.

• Develop and refine atmospheric models focused on boundary layer physics, wind flow, temperature profiles, and terrain effects, with a particular emphasis on conductor-height wind that drives DLR uplift.
• Adapt and integrate numerical weather prediction (NWP) and physics-based models (WRF, WindNinja, or similar) for localized forecasting along power lines, and integrate AI/ML-based forecast systems where they outperform classical NWP.
• Assimilate boundary-layer observations from WireWarrior sensors, ASOS/METAR, mesonets, and other real-time sources to improve predictive accuracy.
• Collaborate with data science and ML teams to merge physics-based models with machine learning approaches, enhancing forecast reliability and spatiotemporal resolution.
• Evaluate forecast skill against ground truth data. Quantify bias, RMSE, CRPS, and other verification statistics by region, season, and lead time, and feed results back into the pipeline.
• Quantify and address sources of model and data uncertainty, developing robust data assimilation and error-correction strategies to produce confidence intervals for operational decisions.
• Ensure data quality from WireWarrior sensors and third-party feeds, working with hardware, software, and third-party support to apply quality-control procedures and improve sensor accuracy.
• Provide inputs to WireWarrior sensor engineers to improve the next generation of drone-deployed weather and power line sensors.
• Communicate findings and recommendations to data scientists, engineers, utility partners, and internal stakeholders. Defend methodology in technical conversations with utility meteorologists and transmission planners.
• Document methodologies and results, ensuring clarity for stakeholders and long-term maintainability.

• Master's or Doctoral degree in Atmospheric Science, Meteorology, Physics, or a related field. Exceptional candidates with a B.S. plus substantial operational forecasting or modeling experience will be considered.
• In-depth understanding of boundary-layer meteorology, atmospheric physics, or related disciplines.
• Experience with numerical weather prediction models (WRF, HRRR, GFS, ECMWF, or similar) and/or physics-based weather modeling or CFD framework (LBM, WindNinja, OpenFOAM, or similar).
• Proficiency in time-series analysis and working with large environmental datasets.
• Strong Python. You write maintainable, production-grade code. Comfort with xarray, NumPy, pandas, and the scientific Python stack.
• Experience with gridded data formats (GRIB2, NetCDF, Zarr) at scale.
• Strong analytical and problem-solving skills, with the ability to quantify and communicate forecast uncertainty.
• Due to ITAR regulations and Government contract requirements, applicants must be a U.S. citizen

• Experience with boundary-layer wind models or CFD frameworks such as LBM, WindNinja, or OpenFOAm.
• Experience with AI-based forecast systems and a clear view on where they fit in an operational stack.
• Experience with statistical or ML-based downscaling, bias correction, or model output statistics (MOS).
• Experience with ensemble forecasting and probabilistic post-processing.
• Field observation experience with weather data and improvement of field sensors.
• Prior work with NOAA datasets, wind/solar irradiance models, or relevant climate data sources.
• Background in energy systems or utility sector operations, especially around transmission line rating, AAR/DLR adoption, or wildfire risk.
• Familiarity with IEEE 738 conductor thermal rating, or willingness to learn it quickly.
• API development experience (FastAPI, Flask, or similar).
• Front-end or visualization experience (React, Flutter, Plotly, deck.gl) for forecast presentation.

• 120 hrs of paid time off
• Health Insurance

• Short-term disability insurance
• Long-term disability insurance

• Paid holidays
• Flexible work schedule

About the Company