AIM
Technology
SeniorSLAMEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior SLAM Engineer at AIM. Skills: SLAM, State estimation, Sensor fusion, Robotics. Design state estimation systems. Develop state estimation systems”
Industry & Context.
Debugging; Root cause analysis
What They're Looking For.
Must Have
5+ years SLAM systems, 5+ years state estimation, 5+ years localization systems, Bachelor's in Computer Science, Bachelor's in Robotics, Bachelor's in Electrical Engineering, Bachelor's in Aerospace Engineering, Mathematical foundation 3D geometry, Mathematical foundation linear algebra, Mathematical foundation probabilistic robotics, Mathematical foundation kinematics, Mathematical foundation optimization, Expertise Extended Kalman Filters, Expertise Unscented Kalman Filters, Expertise Particle Filters, Expertise GTSAM, Expertise Ceres Solver, Expertise g2o, Experience automated sensor calibration, Experience multi-sensor suites, Programming ability modern C++, Programming ability Python, Experience LiDAR odometry, Experience point cloud registration, Experience large 3D point clouds, Experience coupling IMU data, Experience debugging robotic systems, Experience working robotics
Nice to Have
SLAM systems off-road vehicles, SLAM systems agriculture, SLAM systems mining, SLAM systems heavy machinery, Online calibration algorithms, Complex vehicle kinematics, Severe wheel slip, Severe track slip, Handling map obsolescence
What You'll Do.
Design state estimation systems
Develop state estimation systems
Deploy state estimation systems
Design mapping systems
Develop mapping systems
Deploy mapping systems
Design calibration systems
Develop calibration systems
Deploy calibration systems
Architect multi-sensor fusion algorithms
Develop multi-sensor fusion algorithms
Advance mapping stack
Develop algorithms outlier measurements
Design automated calibration pipelines
Build automated calibration pipelines
Maintain automated calibration pipelines
Develop online calibration algorithms
Develop offline calibration algorithms
Create scalable calibration routines
Implement localization algorithms
Optimize localization algorithms
Build map management tools
Build map alignment tools
Build map distribution tools
Collaborate with perception teams
Collaborate with planning teams
Collaborate with controls teams
Provide stable state estimates
Ensure SLAM outputs interact safely
Design localization interfaces
Analyze real-world telemetry
Analyze loop-closure failures
Improve system reliability
Identify failure modes
Develop robust solutions
Develop mathematically sound solutions
Validate localization performance
How You'll Work.
Team & Collaboration
Perception teams; Planning teams; Controls teams
Full Job Description
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 al
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