Fractional Machine Learning Lead (Seizure Detection & Prediction)

Location: Chicago, IL - Remote/Hybrid (Local presence preferred)

Type: Fractional / Contract — Part-time, highly autonomous

Experience: 8+ years of relevant experience Target Start: Q4 2026 Please Read Before Applying This is not a generalist Machine Learning, Computer Vision, or NLP role. We are specifically seeking candidates with hands-on experience working with clinical, noisy, multi-channel scalp EEG data, particularly for seizure detection and prediction.

About the Role: We are building an advanced EEG wearable patch for seizure detection and prediction, and we are looking for a highly specialized Fractional Machine Learning Lead to take ownership of algorithm development, validation, real-time inference, and regulated ML processes. This is a fractional contract opportunity with potential for contract-to-hire as we scale our clinical builds and studies in late 2026.

Key Responsibilities: Algorithm Development & Validation: Finalize and validate our seizure detection algorithms using strict patient-level train/validation/test methodologies to prevent data leakage and ensure robust generalization. EEG Signal Processing: Develop and optimize pipelines for noisy, multi-channel ambulatory scalp EEG data. Inference & Deployment: Build and maintain real-time inference pipelines and integrate our ML models with clinical EEG software for hospital and ambulatory applications. Regulated ML / SaMD: Establish traceability, version control, model lineage, and other regulated ML practices aligned with IEC 62304 and our planned FDA 510(k) pathway. Clinical Validation: Collaborate with our clinical, software, and research teams to support clinical validation studies. Research & Publications: Contribute to academic publications and technical documentation related to seizure detection and prediction. Ideal Background 8+ years of relevant experience in machine learning, signal processing, or medical-device technology. Extensive hands-on experience processing non-invasive, ambulatory scalp EEG data and developing seizure detection/prediction systems. Experience developing and validating ML models within regulated medical-device environments. Familiarity with FDA, CE Mark, IEC 62304, and ISO 13485 requirements. Experience working independently as a consultant, fractional technical leader, or senior individual contributor. Strong ability to own technical workstreams with minimal supervision.

Hard Gatekeeper Requirements: Candidates must meet all of the following:

Regulated Medical Device Experience: Personally shipped at least one regulated Class II or Class III medical wearable that successfully achieved FDA clearance or CE Mark approval, such as a CGM, ECG/EEG patch, or smart hearable. Clinical EEG Expertise: Hands-on experience with clinical, noisy, multi-channel scalp EEG, specifically in seizure detection or prediction. Experience limited to invasive BCI or clean laboratory EEG is not sufficient. Regulatory Usability Engineering: Proven experience authoring IEC 62366 usability engineering documentation for an actual regulatory submission. Electrode & Adhesive Expertise: Deep technical knowledge of skin-electrode interfaces, impedance characterization/testing, motion-artifact mitigation, and medical adhesive behavior. Hardware Collaboration: Ability to support hardware testing and collaborate with our Chicago-based engineering team. This is a hands-on engineering role, not a high-level project management position. Candidates who do not meet the above requirements will not be considered.