Fractional Machine Learning Lead

Theta Neurotech; Chicago (IL); Hybrid; USD 83,000 - 193,000; Part time

Job summary

Theta Neurotech is seeking a Fractional Machine Learning Lead to guide the design and optimization of ML models powering its seizure-prediction system. This part-time hybrid role in Chicago combines technical leadership with collaboration across product and clinical teams.

You will define architectures, implement experiments, and mentor colleagues while advancing robust, real-time EEG analytics in a regulated healthcare environment.

Qualifications

• Strong CS foundation with data structures, algorithms, and software engineering principles.
• Hands-on ML/DL experience with production or research deployments.
• Statistics expertise for experimental design and model evaluation on noisy biosignals.
• Experience designing real-time inference algorithms, preferably in signal processing or biomedical apps.
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow).
• Experience with EEG/time-series data and knowledge of healthcare environments is a plus.
• Excellent communication to document decisions clearly; MS/PhD or equivalent.

Responsibilities

• Define model architectures for seizure prediction.
• Implement and evaluate ML/DL algorithms on EEG data.
• Collaborate with product and clinical partners.
• Establish data preprocessing and feature engineering practices.
• Mentor team members and contribute to roadmaps.

Skills

CS fundamentals

ML/DL experience

Statistics

Time-series data

Python

PyTorch

TensorFlow

Communication

Education

MS/PhD in CS/EE/DS

Tools

Python

SciPy

NumPy

Job description

Company Description Theta Neurotech is developing a wearable EEG patch designed to predict epileptic seizures 30–60 minutes before onset. The company focuses on improving quality of life for the 1.1 million Americans and 30 million people globally living with drug-resistant epilepsy. By enabling preventative drug deployment, Theta Neurotech aims to reduce seizure-related risks and support more independent lifestyles. Applicants will join an early-stage team working at the intersection of neuroscience, medical devices, and advanced machine learning to build clinically meaningful technology.
Role Description The Fractional Machine Learning Lead will provide part-time technical leadership for the design, development, and optimization of ML and deep learning models that power Theta Neurotech’s seizure prediction system. Day-to-day responsibilities include defining model architectures, implementing and evaluating algorithms, and collaborating with product and clinical partners to translate EEG data into robust predictive insights. The role also involves establishing best practices for data preprocessing, feature engineering, experimentation, and deployment, as well as mentoring team members and contributing to technical roadmaps. This is a part-time hybrid role based in Chicago, IL, with a mix of on-site collaboration and work-from-home flexibility.
Qualifications

• Strong foundation in Computer Science, including data structures, algorithms, and software engineering principles.
• Hands-on experience in Machine Learning and Deep Learning, with a track record of building and deploying models in production or research settings.
• Proficiency in Statistics for experimental design, model evaluation, and analysis of noisy physiological or time-series data.
• Ability to design and optimize Algorithms for large-scale or real-time inference, preferably in signal processing or biomedical applications.
• Advanced programming skills in languages such as Python, and experience with ML frameworks (e.g., PyTorch, TensorFlow).
• Experience working with time-series, biosignal, or EEG data, and familiarity with healthcare or regulated environments is beneficial.
• Effective communication skills, with the ability to explain technical concepts to cross-functional partners and document decisions clearly.
• Graduate-level degree (MS or PhD) in Computer Science, Electrical Engineering, Data Science, or a related quantitative field, or equivalent practical experience.