Develop cutting-edge radar-based systems for vital signs inference and contextual activity tracking in healthcare
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.
- 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.
- Collaborate with hardware and embedded software engineers to ensure seamless integration between ML models and radar systems.
Other
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Applied Mathematics, or related field.
- 5–7 years of professional experience in applied AI/ML, ideally with significant experience in startups.
- 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.
