Computer Vision Research Intern at Aquabyte in San Francisco Bay Area

Summary: Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. As a Research Intern, you will collaborate closely with scientists and engineers on the AI team to work on an end-to-end research project at the intersection of computer vision and aquaculture. Responsibilities: - Work with your mentors to complete an end-to-end R&D Project over 10-12 weeks, from literature review through experimentation and a final deliverable - Work on computer vision challenges in 3D reconstruction, efficient inference, visual recognition or model robustness - applied to real production data - Design and run experiments, iterating with increasing depth - Deliver a final presentation and a clean, elegant, reproducible codebase - Collaborate with the wider team on translating research insights into production-ready approaches Required Qualifications: - Enrolled in Masters or PhD program in computer science, electrical engineering, or a related field with a focus on Machine Learning or Computer Vision - Strong coding ability; strong grasp of Python - Have exposure to software engineering best practices (version control, testing, code reviews) - Experience with training neural networks / deep learning (ideally pytorch) - High degree of initiative; comfortable working autonomously on open-ended problems - Must return to degree program after the completion of the internship, or the internship fulfills a graduation requirement Preferred Qualifications: - Experience with stereo vision and 3D reconstruction - Familiarity with vision foundation models and feature extraction/matching - Experience with efficient inference and model optimization - Familiar with classical and deep learning based feature extraction, feature matching, and disparity estimation - Publication record in computer vision or machine learning Required Skills: Machine Learning, Computer Vision, Python, Software Engineering Best Practices, Neural Network Training, Deep Learning, PyTorch, Stereo Vision, 3D Reconstruction, Vision Foundation Models, Feature Extraction, Feature Matching, Model Optimization, Autonomy