Combine boundary-layer meteorology, numerical weather prediction, and production-grade software development to operationalize the forecasts that drive WireWeather's grid-aware line ratings.
DescriptionAs 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.
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, 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.
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, 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
Desired Qualifications- 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.
Benefits- 120 hrs of paid time off
- Health Insurance
- Dental & Vision Insurance
- Short-term disability insurance
- Long-term disability insurance
- Health Savings Account (HSA)
- Flexible Spending Account (FSA)
- Paid holidays
- Flexible work schedule
