About the role
AIM Intelligent Machines | Autonomous Earthmoving Safety First Real-World Results Rapid Deploy Our Team FAQ News Careers DEPLOY NOW Safety First Real-World Results Rapid Deploy Our Team FAQ News Careers Careers Senior SLAM Engineer Senior SLAM Machine Learning Engineer
About us AIM builds autonomy for the real world - robots that move mountains. Our systems fuse software, hardware, robotics, and mission-critical infrastructure into ruggedized, safety-critical machinery operating on jobsites across the world. We replace decades of manual, error-prone, high-risk work with intelligent machines that reshape how earthmoving is done. Localization and mapping are core capabilities of our autonomy platform. Our machines must know precisely where they are in complex, constantly changing environments: terrain that is being actively dug, moved, and reshaped by the machines themselves. Unlike road vehicles that can rely on static HD maps and distinct lane lines, AIM machines operate in dynamic, often feature-poor landscapes. This creates novel challenges in Simultaneous Localization and Mapping (SLAM), state estimation, and sensor fusion. We’re building the SLAM systems that allow machines to navigate reliably, build accurate topographical representations on the fly, and operate safely under harsh physical conditions. We’re growing fast, scaling globally, and building the engineering foundation that will define the next century of construction. About you You’re an engineer who is ready to take one of the most difficult state estimation and mapping problems where algorithmic theory meets the messy, physical world. You have experience building production SLAM or state estimation systems that are proven to work on real hardware. You understand how localization algorithms behave under real-world constraints such as severe sensor vibration, track/wheel slip, GPS-denied environments, and featureless terrain. You enjoy working across the full localization stack - from designing sensor configuration, integration and calibration (IMU, LiDAR, GNSS, kinematics), through factor graph optimization and map management, to deployment on edge compute for real-time control loops. You take ownership of outcomes, not just algorithms. You debug deeply, validate rigorously, and iterate quickly using field data to continuously improve system robustness. You’re motivated by building state estimation systems that enable safe, reliable autonomy in environments where failure is not acceptable.
