SLAM Engineer

Job Description

We're Hiring: SLAM Engineer Full-Time | Core Team LAT Aerospace | Perception & Autonomy At LAT Aerospace, we?re building India?s first clean-sheet hybrid-electric STOL aircraft and a next-generation autonomy stack powering aircraft for complex missions. Our team is kicking off a new chapter ? developing the full flight-control, obstacle-avoidance, GNSS-denied navigation, and swarm-coordination layers that will define LAT?s autonomy architecture. The SLAM layer is the backbone of that mission ? enabling precise real-time localization, mapping, and relative positioning for single-vehicle and swarm scenarios. What you?ll do? Build real-time SLAM and localization pipelines for UAVs operating in fully or partially GPS-denied environments. Fuse multiple perception streams ? camera (mono/stereo), IMU, lidar, depth sensors, altimeters, and terrain databases ? into consistent state estimates. Develop relative-position estimation for multi-agent systems: identifying, tracking, and localizing other drones in a swarm using visual/lidar cues. Integrate terrain-relative navigation (TRN) and map-matching algorithms to achieve sub-meter localization accuracy. Design robust VIO/VSLAM architectures that work in low-light, motion-blur, and feature-poor environments. Implement and optimize EKF/UKF-based sensor fusion or factor-graph methods for real-time UAV operation. Build and maintain simulation and evaluation pipelines for validating localization accuracy under varied conditions (urban, rural, low-altitude, high-dynamics). Iterate through real-world flight testing to refine robustness, latency, and compute performance on embedded hardware. What we're looking for? Strong background in SLAM, VIO, VSLAM, or multi-sensor fusion for robotics or UAVs. Proficiency with C++ and/or Python for real-time SLAM and computational geometry. Hands-on experience with visual SLAM frameworks (ORB-SLAM, VINS-Fusion, OKVIS, ROVIO, Kimera, etc.). Expertise in state estimation using EKF/UKF, factor graphs (GTSAM, Ceres), or graph-based optimization. Experience working with camera + IMU fusion, calibration, synchronization, and noise modelling. Familiarity with lidar processing, scan matching, ICP/NDT, or lidar-inertial fusion pipelines. Understanding of 3D geometry, reprojection, depth estimation, and perception for robotics. Knowledge of embedded compute constraints and techniques for optimizing algorithms for real-time deployment. Why LAT? Core-team role building the autonomy brain of India?s most ambitious aerospace startup. Ownership, speed, and the opportunity to define a new class of systems from scratch.