Senior Applied ML Scientist, DSP job opportunity at Gridware.

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Gridware Senior Applied ML Scientist, DSP
Experience: 3-years
Pattern: hybrid
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Automation

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degreePhD
loacation San Francisco, California, United States Of America
loacation San Francisco,..........United States Of America

We are seeking a Senior Applied #Scientist with expertise in #machine #learning and #digital signal processing (DSP) 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. __ Responsibilities 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. __ Required Skills MS or PhD in Computer Science, Electrical Engineering, or a related technical field. 3+ years of experience developing and deploying production ML models. 3+ years of applied research experience in ML, DSP, or algorithm development. Hands-on experience working with physical sensors and modeling time-series data. Strong foundation in ML architectures, DSP theory, and algorithm design for real-world systems. __ Bonus Skills Experience developing or optimizing algorithms in 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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