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)

• 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

• 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

• 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

Early‑stage medical device company developing a patented, skin‑worn wearable that enables sleep‑lab–level physiologic monitoring, with a focus on identifying undiagnosed sleep apnea before surgery. Addressing a major perioperative safety gap where a large percentage of patients undergo anesthesia with undetected sleep‑related risk. Building tech that directly improves clinical decision making and patient outcomes. Small team, highly technical, mission‑driven, working with wearable devices, physiologic signal processing, ML, and clinical research through federally funded programs.

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