Lead Machine Learning Scientist

Lead Machine Learning Scientist - Sleep & Physiologic Signal Modeling We are currently pipelining for a Lead Machine Learning Scientist role slated for Q2 2026. This leader will spearhead the development of advanced ML models designed to extract clinically significant risk signals from multi-modal physiological data. This role leads the intelligence layer of a novel at-home physiologic monitoring platform designed to support clinical decision-making in perioperative care. This is a hands-on technical leadership role with direct impact on a federally funded Phase I program. Contractual Engagement: 450 hours (approx. 2.5-3 months) in the United States (Remote) Why This Opportunity Is Different Technical ownership - You lead the ML strategy for the intelligence layer, not just a slice of it Clinically grounded ML - Direct collaboration with sleep medicine and anesthesia experts NIH-backed impact - Your work drives feasibility results for a Phase I grant Signal-rich problems - EEG, ECG, oximetry, motion, real data, real complexity Flexible work options - Remote contract work that balances focus, collaboration, and flexibility Growth- Contribute to early-stage product design with potential to extend to long-term roles What You'll Do Design, build, and validate ML pipelines for multi-signal physiologic data modeling Develop robust feature extraction methods for EEG, ECG, pulse oximetry (SpO₂), and motion signals Train and evaluate models to estimate clinically relevant metrics such as arousal burden, hypoxic burden, arousal threshold, and airway instability Collaborate closely with clinical domain experts (sleep medicine and anesthesia) to translate physiologic signals into operational risk signatures Assess model performance, interpretability, and generalizability across patient populations Prepare technical methods, results, and documentation for NIH deliverables, publications, and regulatory-facing materials What You Bring Prefer MS or PhD in Machine Learning, AI, Biomedical Engineering, Computational Neuroscience Hands-on experience modeling physiologic signals (EEG, ECG, PPG, SpO₂, motion) Strong background in deep learning architectures (CNNs, LSTMs, Transformers) Comfort owning ambiguous technical problems end-to-end Bonus: experience in sleep medicine, anesthesia, or medical devices About: An early-stage medical device company developing a patented, skin-worn wearable that provides hospital-grade physiologic monitoring in a home setting. We are addressing a critical perioperative safety gap by identifying high-risk physiologic signatures in patients before surgery. Our platform translates complex, multi-modal signals into actionable insights that improve anesthesia-related decision making. Small team, highly technical, mission-driven, and working with wearable devices, through federally funded programs. By applying for this job, you agree that we can text you (Standard Rates Apply).

Created: 2026-03-10