• Develop and deploy ML models for perception/navigation tasks such as object detection, semantic segmentation, tracking, scene understanding, localization, and path prediction. • Design and implement sensor fusion and mapping pipelines combining vision, depth, LIDAR, IMU, and other signals for robust perception and navigation in dynamic spaces. • Build real-time ML inference pipelines optimized for robotic hardware and embedded compute. • Setup data collection, labeling strategies, dataset curation, and synthetic data augmentation for training and evaluation. • Establish metrics, benchmarks, and test frameworks to validate ML models in both simulation and real-world environments. • Collaborate with robotics software engineers to integrate perception and navigation intelligence into autonomy stacks. • Work with operations to analyze field data, diagnose performance gaps, and iterate on model improvements. • Contribute to long-term ML and perception and navigation architecture decisions, influencing the roadmap for future robots. • Mentor junior ML engineers and contribute to building strong applied ML best practices within the team.