Responsibilities
- Own and optimize the object detection model pipeline, including: Data collection and annotationModel training and validationIntegration into the robot codebaseSystematic testing and performance benchmarking
- Conduct research and development on advanced perception and navigation capabilities, including but not limited to: Investigating and implementing NVIDIA Isaac toolkit functionalities on experimental robots. Profiling various neural networks (e.g., detection, semantic segmentation) on current and next-gen hardware, providing recommendations. Exploring visual localization and mapping techniques to assess feasibility on existing and upcoming hardware platforms. Developing depth map enhancement techniques for commodity stereo cameras
- Collaborate with Computer Vision and Robotics engineers to ensure seamless integration of perception models into robotic systems
- Evaluate and optimize deep learning architectures, balancing accuracy, efficiency, and real-time performance constraints
- Stay up to date with the latest advancements in robotic perception, deep learning, and sensor fusion to drive innovation within the team
Requirements
- 5+ years of experience in robotic perception, computer vision, or deep learning
- Strong proficiency in Python and C++
- Experience working with ROS and ROS2
- Hands-on experience with deep learning frameworks (TensorFlow, PyTorch, TensorRT)
- Familiarity with object detection, semantic segmentation, and depth estimation techniques
- Experience in deploying real-time AI models on embedded systems or robotics hardware
- Strong debugging and optimization skills, especially in a Linux-based development environment
Bonus Qualifications
- Familiarity with NVIDIA Isaac SDK and GPU-accelerated inference pipelines
- Experience working with stereo cameras, LiDAR, and other depth sensors
- Knowledge of visual SLAM, localization, and mapping techniques
- Strong understanding of high-performance computing and embedded AI deployment
- Passion for robotics, automation, and pushing the boundaries of what is possible

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