TensorGo’s interview process begins with resume shortlisting, followed by a take-home assignment—usually involving model fine-tuning or a real-world ML task. After submission, candidates go through two technical interviews focused on deep learning, computer vision, LLMs, and problem-solving skills in DSA. These rounds assess both theoretical knowledge and practical implementation.