AI Researcher – Multimodal Physiological Modeling

Kos Ai • Palo Alto, CA

2d • Onsite

About The Position

KOS AI is developing the next generation of non-invasive metabolic monitoring technology. Our platform combines multimodal sensing, physiological modeling, and advanced machine learning to estimate internal metabolic states continuously and in real time. The work sits at the intersection of biomedical signal processing, artificial intelligence, human physiology, and embedded inference. This role is ideal for a researcher who enjoys solving scientific problems that have no existing solutions and who is motivated by the opportunity to create entirely new algorithms that push the limits of what wearable technology can measure.

Requirements

• Strong foundations in machine learning, time-series modeling, or signal processing
• High proficiency in Python and modern deep learning frameworks
• Experience working with noisy, real-world, or multimodal data
• Ability to explore scientific literature and convert insights into working algorithms
• Curiosity, creativity, and a strong desire to work on difficult open-ended problems

Nice To Haves

• Research experience with biomedical signals or physiological modeling
• Background in optical sensing, human physiology, or biosignal analytics
• Experience working with low-power or resource-constrained inference environments
• Familiarity with probabilistic modeling, uncertainty quantification, or ensemble systems
• Demonstrated ability to design and carry out empirical research

Responsibilities

• Multimodal Signal Processing and Feature Discovery: Development of new feature extraction methods for optical signals, motion signals, and physiological biomarkers; Design of noise-resilient, motion-resilient, and artifact-robust representations; Exploration of frequency-based, time-domain, and hybrid feature spaces; Discovery of patterns that reflect underlying biological processes.
• Machine Learning for Physiological Systems: Research and implementation of models capable of learning from complex, noisy, and high-variability biological data; Development of ensemble systems, temporal models, and context-aware learning frameworks; Design of robust inference pipelines capable of running efficiently on constrained environments; Integration of uncertainty, confidence scoring, and adaptive filtering.
• Biological and Metabolic Dynamics Modeling: Modeling of dynamic processes related to human physiology, metabolism, and behavioral context; Exploration of relationships between physiological signals, daily patterns, and state transitions; Development of personalized data adaptation methods that learn individual physiological baselines and trends.
• Pattern Recognition and Time-Series Forecasting: Research on forecasting trends and physiological trajectories; Identification of behavioral and biological events from multimodal data; Development of systems that adapt to real-world, irregular, and incomplete signals.
• Experimental Research and Dataset Development: Working with diverse datasets collected across multiple conditions; Data cleaning, harmonization, and statistical evaluation; Designing experiments to validate new features, models, and interpretation techniques.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed