Responsibilities
- Investigate and create innovative machine learning models to power next-generation health AI systems, including activity tracking, physiological signal analysis, radar-based movement detection, and voice-driven health assessment.
- Keep up with advancements in machine learning research and quickly test new methods from academic literature, tailoring them to real-world health data and clinical use cases.
- Design and execute experiments with scientific precision, establishing clear hypotheses, controlled evaluations, and reproducible outcomes.
- Adapt and deploy models efficiently across cloud platforms and embedded devices used in clinical monitoring systems.
- Collaborate with operations and backend engineering teams to ensure models satisfy production standards for speed, resource usage, stability, and ease of maintenance.
- Optimize models for low-resource environments using techniques such as quantization, knowledge distillation, and lightweight architectures.
- Partner with clinical researchers to plan validation studies, set performance metrics, and generate evidence required for regulatory clearance.
- Collaborate on defining forward-looking technical and data needs with clinical and signal processing experts to align research with medical impact.
- Record methodologies, experimental findings, and model design choices for use by internal teams and external stakeholders.
- Communicate research outcomes clearly to both technical and non-technical audiences, including clinical collaborators and executive leadership.
- Support the creation of scientific publications, regulatory documentation, and technical white papers as required.
