About the role
Lead the design, development, and integration of perception and navigation systems for robotic platforms, including SLAM, localization, mapping, sensor fusion, obstacle detection, semantic scene understanding, and motion planning interfaces. Develop robust state estimation and localization pipelines using data from cameras, depth sensors, LiDAR, IMUs, wheel odometry, proprioception, GPS, force/torque sensors, or other robot sensor modalities. Build spatial representations for navigation and planning, including occupancy maps, semantic maps, costmaps, 3D scene representations, traversability maps, object-level representations, and dynamic environment models. Integrate perception outputs with planning, control, simulation, and fleet operations systems to support safe and reliable robot autonomy in real-world environments. Architect perception-navigation pipelines that meet practical constraints for latency, bandwidth, compute efficiency, reliability, calibration drift, sensor degradation, and environmental variability. Develop evaluation frameworks, benchmarks, and telemetry-driven analysis methods to measure perception, localization, mapping, and navigation performance across simulation, lab, and field deployments. Partner with learning engineers, spatial AI engineers, simulation engineers, and platform engineers to connect classical robotics pipelines with modern AI models, including learned perception, physical world models, and data-driven navigation approaches. Analyze field logs, sensor data, planner behavior, and failure cases to identify root causes, improve system robustness, and drive cross-stack improvements across perception, planning, controls, and hardware integration. Lead technical design reviews, establish engineering patterns, and mentor other engineers on production-quality robotics autonomy development, testing, debugging, and deployment practices. Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python Master's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Deep experience developing robotics perception or navigation systems, including SLAM, visual-inertial odometry, LiDAR odometry, sensor fusion, state estimation, mapping, obstacle detection, semantic mapping, or traversability estimation. Systems-level understanding of how perception, localization, mapping, planning, and controls interact in deployed autonomous systems, including the failure modes and trade-offs that emerge at integration boundaries. Proficiency in C++ and Python with experience building performant, testable, production-quality robotics software. Experience with robotics middleware and tools such as ROS/ROS2, Nav2, MoveIt, Drake, Gazebo, Isaac Sim, RViz, Foxglove, or equivalent autonomy development and debugging environments. Experience integrating multi-sensor systems, including calibration, synchronization, sensor modeling, uncertainty handling, and robustness to noisy or degraded sensor inputs. Familiarity with modern perception and spatial AI methods, including 3D computer vision, point cloud processing, neural scene representations, semantic segmentation, object detection and tracking, scene graphs, or learned world models. Familiarity with learning-based navigation, imitation learning for navigation policies, or foundation models applied to planning and decision-making. Experience deploying perception and navigation systems on physical robots and debugging real-world issues such as localization drift, dynamic obstacles, changing lighting, reflective surfaces, network constraints, compute limitations, or environmental ambiguity. Experience designing autonomy evaluation frameworks, simulation-based validation pipelines, scenario-based testing, field test protocols, and quantitative metrics for localization, mapping, perception, and navigation performance. Demonstrated ability to lead cross-disciplinary technical efforts involving robotics hardware, perception, planning, simulation, platform infrastructure, field testing, and AI/ML teams. Published research, open-source contributions, patents, or demonstrated technical leadership in robotics perception, SLAM, navigation, autonomous systems, spatial AI, or physical AI is a plus.
