Meteorology Software Developer

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

About Pitch Aeronautics Inc.

Pitch Aeronautics () is a rapidly growing startup creating game-changing solutions for the utility industry. Pitch has developed a drone to install our line sensor, bird diverters, and other equipment on power lines. Our drone-deployable line sensors wirelessly transmit environmental and line characteristics to an online secure API to help utilities push more power through existing lines, prevent wildfires, and improve grid reliability.

At Pitch we've fostered a collaborative, fun, "get-stuff-done" working environment. We believe in moving fast and creating products and prototypes rapidly. If you're looking for a place where you can make a difference on day one, be empowered to achieve, and love working in small teams, we want to meet you!

Description

As a Meteorology Software Developer at Pitch, you will leverage your background in atmospheric science (especially boundary-layer meteorology and now casting) 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, Wind Ninja, or similar CFD frameworks), national and international NWP outputs (HRRR, GFS, ECMWF, RAP), AI-based forecast systems, and real-time observations from Wire Warrior sensors and public networks. You'll also build the Python pipelines, and APIs that turn that work into a product utilities can rely on.

Responsibilities

• 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, Wind Ninja, 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 Wire Warrior 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 Wire Warrior 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 Wire Warrior 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.

Minimum Qualifications

• 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, Wind Ninja, 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, Num Py, 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

Desired Qualifications

• Experience with boundary-layer wind models or CFD frameworks such as LBM, Wind Ninja, 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…