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Lead Machine Learning Scientist
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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: 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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