Overview
About Gridware
Full job description
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 Description: We are seeking a Senior Applied Scientist, On-Device ML to design models that operate on multimodal time-series sensor data in highly resource-constrained environments. You will develop algorithms that balance accuracy with strict power and memory limits, helping advance the next generation of Gridware’s edge intelligence. This role blends applied research, model optimization, and low-level implementation in collaboration with hardware and firmware teams.
Execute end-to-end ML workflows, including exploratory data analysis, feature engineering, model training, evaluation, and optimization.
Design and evaluate machine learning and DSP algorithms that meet strict power, memory, and latency constraints on embedded hardware.
Conduct research and literature reviews on edge ML, resource-constrained inference, and efficient training techniques.
Partner closely with hardware, firmware, and product teams to ensure seamless integration of models into the full system.
MS or PhD in Computer Science, Electrical Engineering, or a related technical field.
3+ years of experience developing and deploying production ML models, on-device.
3+ years of applied research experience in ML or algorithm development.
Hands-on experience working with physical sensors such as IMU, magnetometer, audio and modeling time-series data.
Strong foundation in ML architectures and on-device algorithm design for real-world systems.
Familiar with DSP algorithms and C/C++ for resource-constrained embedded systems.
Experience porting ML models from Python frameworks to firmware-level implementations.
Familiarity with edge ML tools, quantization, model compression, or on-device inference strategies.
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