Computer Vision Engineer

Mentee Robotics is redefining humanoid automation with an AI-first approach, integrating cutting-edge perception, reasoning, and dexterous manipulation into a fully autonomous humanoid robot that continuously adapts and learns. Our flagship product, Menteebot v3, is designed to seamlessly integrate into industrial, logistics, and retail environments, performing complex tasks with human-like adaptability.

We are looking for an experienced Navigation-focused Computer Vision Engineer to join our team. This role is central to our perception and autonomy stack. You will design, implement, and optimize computer vision pipelines for real-time robotic applications, ensuring the highest level of performance and reliability.

Responsibilities

• Design, develop, and implement robust computer vision pipelines for localization, path planning, etc. primarily using Python/C++.
• Develop, optimize and integrate state-of-the-art models like Segmentation, Detectors and Foundation models into our real-time robotics platform.
• Work extensively with various camera interfaces (e.g., GMSL, MIPI and HSB) and sensor data to extract meaningful information for navigation and manipulation.
• Integrate and test components within the ROS2 framework.
• Optimize vision pipelines for low-latency, high-throughput execution on embedded or edge platforms.

Requirements

• 5+ years of experience as a Computer Vision Engineer, Robotics Software Engineer, or ML Engineer focused on localization and path planning.
• Extensive experience and strong proficiency in Python and C++ – a must-have (strong engineering skills are required).
• Deep understanding and hands-on experience with core computer vision concepts and libraries (e.g., OpenCV, PyTorch/TensorFlow).
• Proven experience working with advanced vision models (e.g., Segmentation, Detectors and Foundation models).
• Experience with robotic operating systems (ROS2) and sensor integration.

Advantages

• Experience with multi-modal data (e.g., fusing camera, LiDAR, and IMU data).
• Experience with containerization and orchestration (Docker).
• Deep understanding of Linux and system-level performance optimization.
• Experience with GPU programming (e.g., CUDA).