Gatik AI Inc.
Senior/Staff Software Engineer, Localization and Mapping Mountain View, CA
Gatik AI Inc., Mountain View, California, us, 94039
Who we are
Gatik, the leader in autonomous middle mile logistics, delivers goods safely and efficiently using its fleet of light & medium-duty trucks. The company focuses on short-haul, B2B logistics for Fortune 500 customers including Kroger, Walmart, Tyson Foods, Loblaw, Pitney Bowes, Georgia-Pacific, and KBX; enabling them to optimize their hub-and-spoke supply chain operations, enhance service levels and product flow across multiple locations while reducing labor costs and meeting an unprecedented expectation for faster deliveries. Gatik’s Class 3-7 autonomous box trucks are commercially deployed in multiple markets including Texas, Arkansas, and Ontario, Canada.
About the role We are seeking a Senior/Staff Software Engineer to join our Localization and mapping team to build state-of-the-art mapping pipelines that combine data from a wide variety of sensors, as well as localization algorithms that make use of these maps in real time. You will play a critical part in building robust, real-time localization solutions, leveraging lidar-based odometry to achieve high accuracy in diverse and challenging environments.
This role is onsite at our Mountain View, CA office.
What you'll do
Design, develop, and optimize lidar-based localization and odometry algorithms to improve the positioning accuracy of autonomous vehicles.
Implement algorithms for sensor fusion, utilizing data from Lidar, IMU, GPS, and other sensors to create a robust, reliable localization pipeline.
Develop and integrate real-time SLAM and point cloud processing techniques for both structured and unstructured environments.
Collaborate closely with perception, mapping, and control teams to ensure the seamless integration of localization outputs within the autonomous driving stack.
Conduct rigorous testing and validation in both simulation environments and real-world scenarios, focusing on challenging conditions like urban and unstructured environments.
Analyze and debug complex issues in localization, implementing improvements based on both field test data and user feedback.
Research state-of-the-art techniques in Lidar-based localization, keeping the localization stack up-to-date with advancements in sensor processing and computational efficiency.
Contribute to performance benchmarking, tuning, and continuous improvement efforts to meet real-time constraints and increase system robustness.
What we're looking for
Master’s or Bachelor’s degree in Robotics, Computer Science, Electrical Engineering, or a related field.
6+ years industry experience writing C++ software in a production environment - architecture design, unit testing, code review, algorithm performance trade-offs etc.
Experience in developing localization algorithms for autonomous vehicles or robotic systems, with a strong focus on lidar odometry and SLAM.
Proficiency in C++ and Python, with hands-on experience in ROS/ROS2 and point cloud processing libraries (e.g., PCL).
Experience with pose estimation, SLAM, probabilistic filtering, non-linear optimization and 3D data.
Practical experience in processing large-scale and real-world data.
Experience implementing mathematical principles effectively in software; experienced in Eigen, Ceres/G2O/GTSAM, Boost, etc.
Expert-level knowledge of SLAM - frontend and backend, point cloud registration, GNSS/INS.
Experience with sensor fusion techniques, particularly integrating Lidar, IMU, and GPS data for enhanced localization accuracy.
Experience with performance optimization and real-time system requirements.
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About the role We are seeking a Senior/Staff Software Engineer to join our Localization and mapping team to build state-of-the-art mapping pipelines that combine data from a wide variety of sensors, as well as localization algorithms that make use of these maps in real time. You will play a critical part in building robust, real-time localization solutions, leveraging lidar-based odometry to achieve high accuracy in diverse and challenging environments.
This role is onsite at our Mountain View, CA office.
What you'll do
Design, develop, and optimize lidar-based localization and odometry algorithms to improve the positioning accuracy of autonomous vehicles.
Implement algorithms for sensor fusion, utilizing data from Lidar, IMU, GPS, and other sensors to create a robust, reliable localization pipeline.
Develop and integrate real-time SLAM and point cloud processing techniques for both structured and unstructured environments.
Collaborate closely with perception, mapping, and control teams to ensure the seamless integration of localization outputs within the autonomous driving stack.
Conduct rigorous testing and validation in both simulation environments and real-world scenarios, focusing on challenging conditions like urban and unstructured environments.
Analyze and debug complex issues in localization, implementing improvements based on both field test data and user feedback.
Research state-of-the-art techniques in Lidar-based localization, keeping the localization stack up-to-date with advancements in sensor processing and computational efficiency.
Contribute to performance benchmarking, tuning, and continuous improvement efforts to meet real-time constraints and increase system robustness.
What we're looking for
Master’s or Bachelor’s degree in Robotics, Computer Science, Electrical Engineering, or a related field.
6+ years industry experience writing C++ software in a production environment - architecture design, unit testing, code review, algorithm performance trade-offs etc.
Experience in developing localization algorithms for autonomous vehicles or robotic systems, with a strong focus on lidar odometry and SLAM.
Proficiency in C++ and Python, with hands-on experience in ROS/ROS2 and point cloud processing libraries (e.g., PCL).
Experience with pose estimation, SLAM, probabilistic filtering, non-linear optimization and 3D data.
Practical experience in processing large-scale and real-world data.
Experience implementing mathematical principles effectively in software; experienced in Eigen, Ceres/G2O/GTSAM, Boost, etc.
Expert-level knowledge of SLAM - frontend and backend, point cloud registration, GNSS/INS.
Experience with sensor fusion techniques, particularly integrating Lidar, IMU, and GPS data for enhanced localization accuracy.
Experience with performance optimization and real-time system requirements.
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