Pitch Aeronautics
Machine Learning Engineer (part-time / Idaho-based)
Pitch Aeronautics, Boise, Idaho, United States, 83708
Machine Learning Engineer (part-time / Idaho-based)
Boise, ID
Pitch Aeronautics Inc. (www.pitchaero.com) is a rapidly growing startup creating game-changing solutions for the utility industry. We have developed a drone to install line sensors, bird diverters, and other equipment directly onto power lines. Our drone‑deployable line sensors wirelessly transmit real‑time environmental and line data to a secure online platform—helping utilities push more power through existing lines, reduce wildfire risk, and improve grid reliability.
We’re seeking a talented
Machine Learning Engineer
to help us harness weather and environmental data to build better forecasting models and drive smarter grid operations. This role focuses specifically on developing and deploying ML models that analyze weather patterns, forecast conditions along transmission lines, and support real‑time decision‑making for utility operators.
At Pitch, we foster a collaborative, fun, “get‑stuff‑done” work environment. We move fast, prototype quickly, and empower team members from day one. If you want to shape next‑generation climate‑aware energy infrastructure, we’d love to meet you.
Role Description This is a
part‑time, on‑site role based in Boise, Idaho . As an
ML Engineer focused on weather data and forecasting , you will design and deploy machine learning models that improve our ability to predict wind, temperature, solar radiation, and other environmental factors along high‑voltage transmission corridors. Your models will power our analytics platform, enabling more accurate
Dynamic Line Ratings (DLR)
and helping utility companies mitigate wildfire and outage risks.
You’ll work across weather datasets, time‑series sensor data, and geospatial models to extract insights that improve operational planning and real‑time decision‑making.
Responsibilities
Develop machine learning models that forecast weather and environmental conditions (wind speed, ambient temperature, solar radiation, etc.) at high spatial and temporal resolution
Integrate real‑time weather data, forecast models, and sensor data into predictive pipelines that support grid planning and risk analysis
Apply time‑series analysis, ensemble learning, and probabilistic modeling techniques to generate high‑confidence forecasts
Work closely with hardware and software teams to ensure models are effectively integrated into our analytics platform
Build scalable data pipelines for ingesting, cleaning, and processing weather and IoT sensor data
Quantify uncertainty in model outputs and develop confidence intervals for DLR recommendations
Collaborate with product managers and utility partners to refine use cases and tailor models to real‑world needs
Optimize and deploy ML models using AWS tools and cloud infrastructure (e.g., SageMaker, Lambda, EC2)
Document model methodologies, assumptions, and performance for internal and customer use
Minimum Qualifications
Bachelor’s or Master’s degree in Computer Science, Atmospheric Science, Data Science, Machine Learning, or a related field
Strong experience in machine learning using Python (Scikit‑learn, XGBoost, TensorFlow, or PyTorch)
Familiarity with weather forecasting models, meteorological datasets, or climate modeling frameworks
Experience working with time‑series data, regression models, and forecasting techniquesAbility to build data pipelines for large datasets, especially geospatial or sensor‑based data
Strong analytical and problem‑solving skills, with attention to uncertainty quantification and model validation
Experience with AWS tools (SageMaker, EC2, Lambda, S3, etc.) for model development and deployment
Must be a U.S. citizen or lawful permanent resident (due to ITAR/government contract requirements)
Desired Qualifications
Experience with physics‑informed machine learning, Gaussian Processes, or Bayesian modeling
Prior work with NOAA datasets, wind/solar irradiance models, or numerical weather prediction (NWP) systems
Background in energy systems, utilities, or climate risk modeling
Currently located in Boise or the Treasure Valley area
Experience building models for operational decision‑making in real‑time environments
Learn More About Our Company
Our website: https://www.pitchaero.com/
Our Linked‑In posts: https://www.linkedin.com/company/11764600/
Our Facebook posts: https://www.facebook.com/pitchaero
Video of a drone performing a sensor installation on an energised power line: https://youtu.be/S9F0jz4eqNY?feature=shared
Apply for Machine Learning Engineer (part‑time / Idaho-based)
#J-18808-Ljbffr
Pitch Aeronautics Inc. (www.pitchaero.com) is a rapidly growing startup creating game-changing solutions for the utility industry. We have developed a drone to install line sensors, bird diverters, and other equipment directly onto power lines. Our drone‑deployable line sensors wirelessly transmit real‑time environmental and line data to a secure online platform—helping utilities push more power through existing lines, reduce wildfire risk, and improve grid reliability.
We’re seeking a talented
Machine Learning Engineer
to help us harness weather and environmental data to build better forecasting models and drive smarter grid operations. This role focuses specifically on developing and deploying ML models that analyze weather patterns, forecast conditions along transmission lines, and support real‑time decision‑making for utility operators.
At Pitch, we foster a collaborative, fun, “get‑stuff‑done” work environment. We move fast, prototype quickly, and empower team members from day one. If you want to shape next‑generation climate‑aware energy infrastructure, we’d love to meet you.
Role Description This is a
part‑time, on‑site role based in Boise, Idaho . As an
ML Engineer focused on weather data and forecasting , you will design and deploy machine learning models that improve our ability to predict wind, temperature, solar radiation, and other environmental factors along high‑voltage transmission corridors. Your models will power our analytics platform, enabling more accurate
Dynamic Line Ratings (DLR)
and helping utility companies mitigate wildfire and outage risks.
You’ll work across weather datasets, time‑series sensor data, and geospatial models to extract insights that improve operational planning and real‑time decision‑making.
Responsibilities
Develop machine learning models that forecast weather and environmental conditions (wind speed, ambient temperature, solar radiation, etc.) at high spatial and temporal resolution
Integrate real‑time weather data, forecast models, and sensor data into predictive pipelines that support grid planning and risk analysis
Apply time‑series analysis, ensemble learning, and probabilistic modeling techniques to generate high‑confidence forecasts
Work closely with hardware and software teams to ensure models are effectively integrated into our analytics platform
Build scalable data pipelines for ingesting, cleaning, and processing weather and IoT sensor data
Quantify uncertainty in model outputs and develop confidence intervals for DLR recommendations
Collaborate with product managers and utility partners to refine use cases and tailor models to real‑world needs
Optimize and deploy ML models using AWS tools and cloud infrastructure (e.g., SageMaker, Lambda, EC2)
Document model methodologies, assumptions, and performance for internal and customer use
Minimum Qualifications
Bachelor’s or Master’s degree in Computer Science, Atmospheric Science, Data Science, Machine Learning, or a related field
Strong experience in machine learning using Python (Scikit‑learn, XGBoost, TensorFlow, or PyTorch)
Familiarity with weather forecasting models, meteorological datasets, or climate modeling frameworks
Experience working with time‑series data, regression models, and forecasting techniquesAbility to build data pipelines for large datasets, especially geospatial or sensor‑based data
Strong analytical and problem‑solving skills, with attention to uncertainty quantification and model validation
Experience with AWS tools (SageMaker, EC2, Lambda, S3, etc.) for model development and deployment
Must be a U.S. citizen or lawful permanent resident (due to ITAR/government contract requirements)
Desired Qualifications
Experience with physics‑informed machine learning, Gaussian Processes, or Bayesian modeling
Prior work with NOAA datasets, wind/solar irradiance models, or numerical weather prediction (NWP) systems
Background in energy systems, utilities, or climate risk modeling
Currently located in Boise or the Treasure Valley area
Experience building models for operational decision‑making in real‑time environments
Learn More About Our Company
Our website: https://www.pitchaero.com/
Our Linked‑In posts: https://www.linkedin.com/company/11764600/
Our Facebook posts: https://www.facebook.com/pitchaero
Video of a drone performing a sensor installation on an energised power line: https://youtu.be/S9F0jz4eqNY?feature=shared
Apply for Machine Learning Engineer (part‑time / Idaho-based)
#J-18808-Ljbffr