Computer Vision Intern (Deep Learning)

This track prioritises the architectural training of segmentation and detection models to advance our foundational layer of visual intelligence. Responsibilities Develop and train deep learning architectures like YOLO, Faster RCNN, and UNet for perception tasks. Assist in adapting foundation models such as CLIP, DINO, and SAM for robotic use cases. Maintain experiment logs and conduct failure analysis on model performance metrics. Apply TorchVision and Albumentations within Python workflows for model augmentation. Minimum Qualifications Technical proficiency in PyTorch or TensorFlow frameworks Rigorous understanding of probability, calculus, and linear algebra Exposure to vision-language or self-supervised models is advantageous Why Build With Us? Collaborate directly with IIT/IISc founders in a high-density engineering environment. Solve complex "zero-to-one" problems in Physical AI and autonomous systems. Develop dual-use technology for national defence (GPS-denied navigation) and industrial automation. Access competitive compensation, paid time off, and a growth-focused culture. Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.