Senior ML Engineer, Multi-Sensor Modeling

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.

Develop algorithms that improve the speed, accuracy, and reliability of Gridware’s automated hazard detection systems Work with multimodal time-series and spatial sensor data across diverse sampling rates and noise characteristics. Design models that are robust, interpretable, and deployable in production environments. Live in the data; help curate & share strategic & well-defined datasets that help solve our highest-value challenges Explore advanced approaches such as graph-based learning for grid topology reasoning, geospatial modeling and localization and multimodal fusion across acoustic, magnetic, vibration, electrical, and visual signals Production Engineering Write clean, scalable, well-tested Python code that integrates into a large shared codebase. Build end-to-end ML pipelines including data processing, feature extraction, training, evaluation, and deployment. Optimize models for performance, reliability, and real-world constraints. Collaborate on infrastructure for model monitoring, validation, and continuous improvement. Collaboration & Communication Translate complex analyses into clear insights for engineers, operators, and leadership. Frame solutions to ambiguous, open-ended problems to achieve buy-in from various stakeholders by focusing on the business impact of your projects Communicate uncertainty, tradeoffs, and model behavior effectively. Partner cross-functionally with software, data engineering, product, and event-reporting teams. Help shape technical direction and best practices for ML at Gridware. This includes exemplifying standards for experiment tracking, model versioning, reproducibility, and lifecycle management.

5+ years of experience in machine learning, signal processing, or applied physics in production environments. Strong programming skills in Python and experience contributing to large, shared codebases. Experience working within modern software stacks, including cloud platforms, containerization, and CI/CD workflows Excellent written and verbal communication, especially explaining data and models clearly.

Experience with Graph Neural Networks or learning over physical/topological systems. Familiarity with power systems, embedded sensing, or edge ML. Proven experience with time-series modeling and noisy real-world sensor data. Experience with multimodal learning or sensor fusion. Track record of technical leadership or mentoring.

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