Fractional Machine Learning Lead (Seizure Detection & Prediction)

Stealth Startup; Chicago (IL); Hybrid; USD 96,000 - 165,000; Part time

Job summary

Stealth Startup in Chicago is seeking a highly specialized Fractional Machine Learning Lead to own algorithm development, validation, real-time inference, and regulated ML practices for an EEG seizure detection platform.

This hands-on role requires deep experience with ambulatory scalp EEG data, regulatory submissions, and cross-functional collaboration with clinical and hardware teams in a fast-paced startup environment.

Qualifications

• 8+ years in ML, signal processing or medical-device tech.
• Hands-on experience with clinical, ambulatory scalp EEG data.
• Experience in regulated medical-device environments.
• Independent consultant or fractional leadership experience.
• Familiarity with IEC 62304 and ISO 13485.

Responsibilities

• Algorithm development & validation for seizure detection with patient-level train/validation/test methodologies.
• EEG signal processing for noisy, multi-channel scalp EEG data.
• Inference & deployment: real-time pipelines and integration with clinical EEG software.
• Regulated ML / SaMD: establish traceability, version control, model lineage.
• Collaborate with clinical and research teams for validation studies.
• Contribute to academic publications and technical documentation.

Skills

EEG signal processing

Seizure detection

Regulated ML/healthcare

Tools

IEC 62304

FDA processes

ISO 13485

Job description

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.

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.