Meteorology Software Developer

Role overview

As a Meteorology Software Developer at Pitch Aeronautics, you’ll combine boundary-layer meteorology and numerical weather prediction with production-grade software development to operationalize forecasts for grid-aware line ratings. The role splits roughly evenly between meteorology work and software engineering.

What you’ll do

• Develop, refine, and operationalize high-resolution stochastic wind and temperature forecasts along power lines.
• Feed forecasts into Dynamic Line Rating (DLR), Ambient-Adjusted Rating (AAR), Contingency Line Rating (CLR), and wildfire/outage-risk tools.
• Integrate physics-based models (e.g., WRF, WindNinja or similar CFD frameworks) and NWP outputs (e.g., HRRR, GFS, ECMWF, RAP).
• Incorporate AI/ML forecast systems when they outperform classical NWP.
• Assimilate real-time observations from WireWarrior sensors and public networks (e.g., ASOS/METAR, mesonets) to improve accuracy.
• Build Python pipelines and APIs to turn modeling and data into reliable products utilities can use.
• Evaluate forecast skill using metrics such as bias, RMSE, and CRPS, and feed verification results back into the pipeline.
• Quantify uncertainty and develop data assimilation and error-correction strategies, including confidence intervals for operational decisions.
• Ensure data quality from WireWarrior sensors and third-party feeds with appropriate quality-control procedures.
• Provide feedback to sensor engineers to improve next-generation drone-deployed weather and power line sensors.
• Communicate findings to data scientists, engineers, utility partners, and internal stakeholders; document methods for maintainability.

Minimum qualifications

• Master’s or Doctoral degree in Atmospheric Science, Meteorology, Physics, or a related field.

Nice-to-haves (implied)

• Experience working with atmospheric models and NWP/CFD systems.
• Ability to build production Python pipelines and expose results via APIs.
• Familiarity with verification and uncertainty quantification for operational forecasting.