AIM provides an embodied AI platform that retrofits bulldozers, excavators and other heavy equipment with plug-and-play hardware and software to enable autonomous, zero-entry fleets and AI-optimized site operations.
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. Learn more about AIM here. https://aim.vision/about 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. About us together We are solving SLAM problems that do not exist in traditional autonomy domains. AIM machines operate in environments that are constantly evolving - digging soil, moving rock, loading trucks, and reshaping terrain. These environments introduce challenges such as: - Dynamic terrain - Feature-poor environments - Sensor occlusion - Dust, and environmental noise - Vibration and degrading calibration We will design algorithms that perform reliably in these environments. We will build SLAM systems that integrate tightly with perception, planning, controls, and machine operations. And we will continuously close the loop between field data and algorithm improvements. If that excites you - you’re the kind of Senior SLAM Engineer who will thrive here. What you will own As a Senior SLAM Engineer, you will design, develop, and deploy the state estimation, mapping and calibration systems that allow AIM’s autonomous machines to navigate and understand their changing environment. Design & Advance SLAM Systems - Architect and develop robust multi-sensor fusion algorithms (LiDAR, IMU, GNSS, wheel odometry/kinematics) for high-frequency, low-latency state estimation. - Advance AIM’s mapping stack, including point cloud registration, loop closure, and dynamic map updating. - Develop algorithms to detect and react to outlier measurements caused by dust, sliding terrain, or sensor degradation. Own Sensor Calibration Pipelines - Design, build, and maintain automated calibration pipelines for complex multi-sensor rigs (LiDAR, Camera, INS/IMU). - Develop algorithms for online and offline intrinsic, extrinsic, and spatio-temporal calibration to ensure high precision despite severe machine vibration and mechanical wear. - Create scalable calibration routines that can be executed by field operators on active jobsites without needing specialized laboratory environments. Build Production Localization Systems - Implement and optimize multi-modal localization algorithms (graph or filter based) for real-time deployment on edge hardware. - Build scalable tools for map management, alignment, and distribution across fleets of machines. Integrate SLAM with the Autonomy Stack - Collaborate with perception, planning, and controls teams to provide stable, continuous state estimates required for precise, heavy-duty manipulation and navigation. - Ensure SLAM outputs interact safely with machine-centric awareness and safety systems. - Design localization interfaces that are robust, testable, and observable. Use Field Data to Improve System Performance - Analyze real-world telemetry and logs collected from deployed machines to characterize drift, analyze loop-closure failures, and improve system reliability. - Identify edge cases and failure modes and develop robust, mathematically sound solutions. - Work closely with field engineers and operators to validate localization performance under real, punishing operating conditions. Qualifications - Bachelor’s or Master’s degree in Computer Science, Robotics, Electrical Engineering, Aerospace Engineering, or a related field. - 5+ years of professional experience building SLAM, state estimation, or localization systems. - Strong mathematical foundation in 3D geometry, linear algebra, probabilistic robotics, kinematics, and optimization. - Deep expertise in modern state estimation techniques (e.g., Extended/Unscented Kalman Filters, Particle Filters) and optimization frameworks (e.g., GTSAM, Ceres Solver, g2o). - Hands-on experience developing and maintaining automated sensor calibration pipelines (intrinsic, extrinsic, and