Senior/Staff Software Engineer, Localization

Gatik
Mountain View, CA$180k – $250kPosted 27 January 2026

Job Description

Who we are Gatik, the leader in autonomous middle-mile logistics, is revolutionizing the B2B supply chain with its autonomous transportation-as-a-service (ATaaS) solution and prioritizing safe, consistent deliveries while streamlining freight movement by reducing congestion. The company focuses on short-haul, B2B logistics for Fortune 500 retailers and in 2021 launched the world’s first fully driverless commercial transportation service with Walmart. Gatik's Class 3-7 autonomous trucks are commercially deployed across major markets, including Texas, Arkansas, and Ontario, Canada, driving innovation in freight transportation. The company's proprietary Level 4 autonomous technology, Gatik Carrier™, is custom-built to transport freight safely and efficiently between pick-up and drop-off locations on the middle mile. With robust capabilities in both highway and urban environments, Gatik Carrier™ serves as an all-encompassing solution that integrates advanced software and hardware powering the fleet, facilitating effortless integration into customers' logistics operations. About the role We are seeking a Senior/Staff Software Engineer to join our Localization team to build state-of-the-art sensor fusion solutions that combine data from a wide variety of sensors, as well as localization algorithms that integrate with maps in real time. We are looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team. You will play a critical part in building robust, real-time localization solutions, leveraging real-time sensor measurements to achieve high accuracy in diverse and challenging environments. This role is onsite 5 days at our Mountain View, CA office! What you'll do Design, develop, and optimize sensor-based localization and odometry algorithms to improve the positioning accuracy of autonomous vehicles. Implement algorithms for sensor fusion, utilizing data from IMU, GPS, Lidar, and other sensors to create a robust, reliable localization pipeline. Develop and integrate real-time SLAM 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 and camera-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 M.S. or Ph.D. 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. 2+ years of experience in the AV/ADAS industries. Experience in developing localization algorithms for autonomous vehicles or robotic systems, with a strong focus on GPS-IMU sensor fusion and SLAM. Proficiency in C++ and Python, with hands-on experience in ROS/ROS2. Experience with pose estimation, SLAM, probabilistic filtering, Kalman filtering, and non-linear optimization. Practical experience in processing large-scale and real-world data sets. Experience implementing mathematical principles effectively in software Expert-level knowledge of SLAM, GNSS/INS. Experience with sensor fusion and sensor calibration techniques, particularly integrating IMU, GPS, and Lidar data for enhanced localization accuracy. Experience with performance optimization and real-time ... (truncated, view full listing at source)
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