This role blends classical computer vision, modern machine learning, and embedded deployment. You’ll work with autonomy and systems teams to design perception pipelines that balance accuracy, efficiency, and real-time performance—while being ready to go deep when needed.

Responsibilities

• Develop and optimize computer vision and deep learning algorithms for tasks such as object detection, segmentation, and scene understanding.
• Design robust perception modules that work reliably in real-world factory and campus environments.
• Build and manage scalable data pipelines for collection, annotation, training, and validation.
• Deploy and optimize models for edge inference on embedded compute platforms.
• Collaborate with autonomy and controls teams to integrate perception into the full robotic software stack.
• Validate performance in simulation and on real robots; debug and iterate to ensure robustness.
• Maintain clean, modular code with solid documentation and participation in reviews.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Robotics, Electrical Engineering, or a related field.
• 3+ years of strong foundation experience in computer vision, deep learning and machine learning fundamentals.
• Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.
• Proficiency in Python and C++ for algorithm development and deployment.
• Experience with OpenCV, NumPy, and other standard vision libraries.
• Familiarity with ROS/ROS2 and perception module integration within robotic systems.
• Experience with model optimization and deployment tools such as TensorRT, ONNX, or quantization workflows.
• Solid debugging and system-level problem-solving skills, especially in real-time and embedded contexts.