Company Description Saccade buildsAI-powered tools for the earlyscreening of neurological and mentalhealth disorders. Our proprietaryhardware attachment for VR headsetscaptures eye movement data during abrief five-minute visual task, and ourmodels interpret those patterns tosurface subtle signals thattraditional clinical assessmentsmiss, potentially yearsbefore symptoms appear. Wework directly withclinicians, primary care physicians,and leading technology companies tomake early detection accessible,objective, and scalable across everycare setting. We recently closedourround and are heading into a largerraise. The team is small, the hardwareis in hand, and the first patient datais about to come in.
Role Description As a Computer Vision and Machine Learning Engineering Intern at Saccade, you will design, implement, and optimize the CV and ML pipelines that run on our custom hardware attachment and camera systems. You will work directly with the founders to define technical requirements, build end-to-end pipelines from raw sensor data to production inference, and train and evaluate the first models on real patient data collected under IRB. Day to day, this means prototyping algorithms, improving data quality and labeling workflows, collaborating with clinical advisors on experimental design, and contributing to the Apple Vision Pro application that delivers the test. As one of our engineers, you will own core architectural decisions that stay in the product for years. This is a paid, full-time, on-site internship in San Francisco near Pacific Heights, with a clear path to converting into a founding full-time role.
Qualifications
- Strong foundation in computer science and algorithms, with experience designing efficient, scalable systems and data pipelines
- Hands-on expertise in computer vision, pattern recognition, and neural networks using modern frameworks such as PyTorch or TensorFlow
- Experience building eye tracking technology, ideally with custom camera hardware
- Advanced programming skills in Python or C++, plus solid software engineering practice around testing, version control, and code review
- Proficiency in statistics and experimental design, including model evaluation, hypothesis testing, and working with noisy real-world sensor data
- Currently pursuing or recently completed a degree in Computer Science, Electrical Engineering, Biomedical Engineering, or a related technical field.
- Comfort working on-site in a fast-paced startup environment, with strong problem-solving skills and attention to detail.
- Ability to work on-site in the San Francisco Bay Area, since the role involves physical hardware
- Genuine interest in healthcare, neuroscience, or mental health, and motivation to build tools that improve diagnostic quality and patient outcomes
Preferred
- Prior work with time-series or physiological data
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