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Develop cutting-edge radar-based systems for vital signs inference and contextual activity tracking.

Requirements

• Strong expertise in machine learning & deep learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
• Proficiency in Python for model development and experimentation.
• Strong proficiency in C++, with proven experience deploying models in embedded/edge environments.
• Background in signal processing, with a strong preference for radar systems experience.
• Experience with ML lifecycle management: data collection, cleansing, training, validation, monitoring, drift detection, and retraining.
• Familiarity with healthcare or human monitoring applications.
• Experience with sensor fusion (combining radar with other sensing modalities).

Responsibilities

• Design, train, and deploy machine learning models for radar signal interpretation and human activity/vital signs inference.
• Implement end-to-end ML pipelines including data collection, preprocessing, training, testing, validation, and deployment.
• Manage the ML lifecycle, including monitoring for drift, retraining, and updating models in production environments.
• Apply advanced signal processing techniques to radar data for robust and accurate feature extraction.
• Collaborate with hardware and embedded software engineers to ensure seamless integration between ML models and radar systems.
• Optimize and deploy ML models on embedded hardware platforms, with a focus on C++ edge deployment.
• Build efficient inference pipelines that run within constrained compute environments.

Other

• Work cross-functionally with hardware, software, and clinical teams to define requirements and deliver production-ready solutions.
• Operate independently in a startup setting, proactively solving problems and driving projects from concept to deployment.
• Demonstrated ability to work independently and thrive in fast-paced, startup environments.
• Strong problem-solving skills, creativity, and a can-do attitude.
• Excellent communication skills for collaborating across technical and non-technical teams.