Senior Applied ML Engineer, On-Device

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, on-device.
• 3+ years of applied research experience in ML or algorithm development.
• Hands-on experience working with physical sensors and modeling time-series data.
• Strong foundation in ML architectures and on-device algorithm design for real-world systems.

Bonus Skills

• 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.