Applied Scientist - Circuit and Fault Intelligence

About Gridware

Gridware is a San Francisco-based technology company dedicated to protecting and enhancing the electrical grid. We pioneered a groundbreaking new class of grid management called active grid response (AGR), focused on monitoring the electrical, physical, and environmental aspects of the grid that affect reliability and safety. Gridware’s advanced Active Grid Response platform uses high-precision sensors to detect potential issues early, enabling proactive maintenance and fault mitigation. This comprehensive approach helps improve safety, reduce outages, and ensure the grid operates efficiently. The company is backed by climate-tech and Silicon Valley investors. For more information, please visit www.Gridware.io.

Role Summary

Gridware is creating cutting edge technology to increase hazard awareness on the electric distribution system. We are building the observability layer of a safer and more efficient grid.

We are seeking an Applied Scientist to lead the development of advanced analytical and machine learning models for detecting and interpreting events from distributed sensors installed on electrical distribution infrastructure. You will leverage expertise in the physical sciences and modern machine learning techniques to deliver robust, real-time event detection and classification capabilities.

You will collaborate with a diverse team of scientist and engineers to build the hardware, software, and the operational systems to deliver actionable information to utility operators.

• Develop analytical and computational models to detect, classify, and interpret physical events in data collected from networks of distributed sensors.
• Apply statistical and physics-based modeling techniques to understand signal propagation, noise characteristics, and how events will be represented across multiple observers.
• Collaborate with data engineers and software teams to design robust pipelines for data ingestion, preprocessing, and real-time anomaly detection.
• Validate and refine models using real-world sensor data and experimental validation.
• Design feature extraction methods for complex, multivariate sensor signals using signal processing, physics-based heuristics, and machine learning.
• Conduct simulations and perform sensitivity analyses to test system performance under various conditions.
• Contribute to model interpretability, uncertainty quantification, and decision support tools.

• PhD or Master’s in Physics, Neuroscience, Environmental Science, Applied Math, Electrical Engineering, or a related quantitative field.
• 5+ years of experience working with large-scale scientific or sensor data in applied settings.
• Strong foundation in physical modeling, signal processing, and machine learning.
• Proficiency in Python for scientific computing; experience with scientific libraries (e.g., NumPy, SciPy, Pandas, matplotlib), machine learning libraries (SKLearn, Keras, PyTorch), and data platforms (SQL, Spark, etc.).
• Experience working with noisy, asynchronous, and multi-modal sensor data in real-world environments.
• Ability to design experiments and analyze results to validate models against empirical data.

• Experience with real-time data streaming frameworks (e.g., Kafka, Spark Streaming).
• Exposure to distributed sensing systems in energy, seismic monitoring, aerospace, industrial IoT, or environmental science.
• Familiarity with spatial-temporal modeling, graph-based machine learning, or multi-modal foundation models.
• Prior work involving uncertainty quantification, Bayesian inference, or hybrid physical-statistical models.
• Publications or patents related to sensor data analytics or physical system modeling.

This describes the ideal candidate; many of us have picked up this expertise along the way. Even if you meet only part of this list, we encourage you to apply!

Benefits

Health, Dental & Vision (Gold and Platinum with some providers plans fully covered) 

Paid parental leave 

Alternating day off (every other Monday)

“Off the Grid”, a two week per year paid break for all employees. 

Commuter allowance 

Company-paid training