McCue is seeking to develop and deploy autonomous safety inspection robotics powered by computer vision and AI systems, integrating perception, intelligence, and data into deployable, real-world systems.
Requirements
- Familiarity with transformer-based or multimodal AI models and their application in perception or robotics contexts
- Proficiency with PyTorch or TensorFlow, OpenCV, and common vision frameworks (YOLO, Detectron, etc.)
- Experience developing and deploying AI models on edge devices (Jetson or similar)
- Solid programming ability in Python; familiarity with C++ and ROS2 integration preferred
- Strong understanding of data pipeline design and real-world validation (mAP, precision, recall metrics)
- Experience managing or curating labeled datasets for supervised learning
- Comfortable working in a fast-paced, ambiguous environment with minimal structure
Responsibilities
- Lead and maintain development of the AI and perception architecture for different projects
- Design and train computer vision models (e.g., YOLOv8, segmentation, object detection) for safety inspection tasks
- Manage dataset pipeline: collect, clean, label, and augment data for AI model training using tools like Roboflow or CVAT
- Deploy optimized models to edge computing devices (NVIDIA Jetson Orin NX/Xavier)
- Collaborate with robotics partners to integrate AI perception with sensors, cameras, and ROS2-based navigation systems
- Contribute to design of data and cloud architecture for inference logging, retraining loops, and reporting dashboards
- Validate performance through testing in live industrial environments, iterating to achieve production-level accuracy and reliability in real-world testing
Other
- Bachelor’s or master’s degree in computer science, robotics, electrical engineering, or related field
- 3–6 years of experience in AI, computer vision, or applied machine learning (industrial or robotics applications preferred)
- Strong problem-solving and communication skills with the ability to work cross-functionally
- Comfortable working in a fast-paced, ambiguous environment with minimal structure
- Commitment to equal employment opportunity
