Senior SLAM Engineer - Autonomous Driving

Position Overview

We are seeking a highly skilled and motivated Senior SLAM Engineer to join our Autonomy team. You will lead the development and deployment of robust SLAM (Simultaneous Localization and Mapping) systems that are critical to the safe and efficient operation of our autonomous electric tractors. This is a key technical role that requires deep expertise in SLAM algorithms, sensor fusion, real-time robotics, and system-level deployment on embedded platforms.

Key Responsibilities

• Design, develop, and optimize real-time SLAM systems for dynamic and semi-structured agricultural environments.
• Integrate multi-modal sensor data (stereo/mono cameras, IMU, GPS, wheel encoders) for robust localization and mapping.
• Research and implement state-of-the-art SLAM techniques (graph-based, visual-inertial, Scan Matching).
• Ensure real-time performance and robustness in outdoor agricultural environments with limited features.
• Collaborate with perception, planning, controls, and hardware teams to deliver reliable autonomy stacks.
• Profile, debug, and optimize performance on embedded compute platforms (e.g., NVIDIA Jetson Orin).
• Lead field tests and validation efforts, analyze SLAM performance, and iterate on improvements.
• Mentor junior engineers and contribute to code quality, documentation, and system design.

Required Qualifications

• M.S. or Ph.D. in Robotics, Computer Vision, Computer Science, or related field.
• 5+ years of experience developing SLAM systems in robotics or autonomous vehicle domains.
• Deep understanding of and visual-inertial SLAM (e.g., ORB-SLAM, Cartographer, LOAM, VINS-Fusion).
• Proficiency in C++ and Python; experience with ROS/ROS2.
• Strong knowledge of sensor fusion, Kalman filters, factor graphs, and non-linear optimization (e.g., GTSAM, Ceres).
• Familiarity with agricultural or off-road environments and their challenges (e.g., dust, changing terrain, limited GPS).
• Proven experience deploying SLAM systems on real-world autonomous robots.

Preferred Qualifications

• Experience with embedded systems and real-time SLAM deployment on resource-constrained hardware.
• Familiarity with deep learning-based localization or semantic mapping.
• Background in SLAM benchmarking, ground truthing, and field validation tools.
• Contributions to open-source SLAM or robotics software stacks.