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). Apply tot his job