About the Role

Responsibilities

• Design novel algorithms (event detection, sequence-to-sequence, time-series regression, etc.) to push the boundaries of non-invasive neuromotor interfaces.
• Help software team plan high-impact studies aimed at improving signal extraction, decoding, and classification of EEG/EMG signals. Help software team translate fundamental scientific insights into practical solutions for real-world clinical and consumer applications.
• Work closely with our software and hardware teams to integrate advanced ML pipelines into our Neuralis™ platform, ensuring a seamless transition from prototype to end-user deployment.
• Shape research directions that expand the capabilities of our product suite (e.g., NeuroDiffusion™). Contribute to an iterative process that uses deep learning breakthroughs to enhance non-invasive BCI performance.
• Share your work in peer-reviewed journals and top conferences. Represent Synaptrix Labs at academic and industry events to amplify our mission and discoveries.

Requirements

• Currently pursuing or holding a PhD (or are incredibly cracked) in a field such as deep learning, artificial intelligence, machine learning, computer science, robotics, computational neuroscience, signal processing, or a closely related domain.
• Strong programming skills in Python, particularly with libraries for scientific computing (e.g., NumPy, SciPy, Pandas) and machine learning (e.g., PyTorch, TensorFlow). Knowledge of lower level programming skills is a plus.
• Solid understanding of quantitative methods—math, statistics, probability—and demonstrated ability to learn new technical concepts quickly.
• Hands-on research experience designing, executing, interpreting, and presenting experiments in an academic or industry setting.
• Ability to work autonomously and collaboratively on cross-functional teams to develop and validate new technology.

Nice to Haves

• Experience analyzing and modeling high-dimensional time series, including neural signals (EEG, EMG), audio, sensor data, or other complex multichannel modalities.
• Track record of building end-to-end ML pipelines for real-time signal processing or interactive systems.
• Familiarity with large-scale cluster computing (e.g., AWS, GCP) for machine learning applications.
• Publications in top-tier conferences and journals showcasing impactful research in deep learning, neuroscience, or biomedical engineering.
• Demonstrated software engineering experience (internship, work experience, open-source contributions) indicating proficiency in production-level code practices.