Robotics AI Engineer – Calibration, Localization, and Mapping

Field AI is transforming how robots interact with the real world. We are building risk‑aware, reliable, and field‑ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data‑driven approaches or pure transformer‑based architectures, and are charting a new course, with already‑globally‑deployed solutions delivering real‑world results and rapidly improving models through real‑field applications.

Location:
Mission Viejo, CA

Field AI is at the forefront of robotic embodied AI, transforming industries like construction, security, mining, and manufacturing. Our autonomous robots operate globally, often in harsh environments, delivering critical insights to customers. Whether monitoring construction progress, ensuring safety compliance, or conducting predictive maintenance, Field AI is advancing technology to make a meaningful impact.

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Field AI is seeking passionate scientists and engineers to play a vital role in the development of advanced geometric robot perception algorithms. As a member of our Calibration, Localization, and Mapping (CLAM) team, you will work with a team of high‑focused individuals in a fast‑paced environment to deliver production‑quality calibration, localization, and mapping subsystems. You will work with a variety of different sensing modalities and help streamline our calibration pipeline and/or develop environment‑agnostic localization and mapping algorithms for our downstream autonomy modules.

The specific project and work that you will be doing will depend on your specific skill set and area of interest. The ideal candidate will not only have a deep theoretical understanding of modern calibration, localization, and mapping algorithms, but also the practical skills to deliver high‑quality and maintainable code on a small, focused team in a fast‑paced environment.

• Design and implement state‑of‑the‑art calibration, localization, and mapping algorithms for our autonomous quadrupeds and humanoids
• Develop various tools and metrics to continuously validate and benchmark the performance of our various subsystems
• Write clean, efficient, and maintainable code in C++ and Python
• Collaborate with cross‑functional teams to integrate and deploy your algorithms into real‑world systems

• Master’s or Ph.D Electrical Engineering, or a related field, with expertise in calibration, localization, and/or 3D mapping
• Strong mathematical foundation in linear algebra, probability theory, numerical optimization, 3D geometry, Lie theory, kinematics
• Strong theoretical understanding and practical experience with different linear and non‑linear state estimation frameworks for sensor fusion
• with state‑of‑the‑art LiDAR‑inertial odometry andli>
• Familiarity with state‑of‑the‑art visual‑inertial odometry and SLAM algorithmsn
• Experience with sensor calibration (intrinsics and extrinsics) and time synchronization
• Strong proficiency in C++ and Python and modern best practices, with write maintainable and efficient code
• Strong understanding how ROS works and experience with designing, building, and deploying your own nodes into a complex robotic system
• Strong problem‑solving skills, attention to detail, and being able to work independently while managing multiple priorities
• Strong work ethic, highly self‑motivated, and excellent written and verbal communication skills

• Publications and/or patents in calibration, state estimation, odometry, and/or SLAM
• Experience working with noisy sensor measurements and designing outlier‑resilient algorithms
• Experience working with computationally constrained platforms and designing efficient, real‑time algorithms with low computational complexity
• Experience with other sensing modalities (e.g., radar, doppler‑LiDAR, GPS, etc.)
• Experience with modern version control and project management tools

Field AI believes in‑person collaboration is essential for tackling complex challenges. This is a fully onsite role based in…