The role focuses on developing real-time navigation, estimation, and control algorithms for complex autonomous platforms operating in dynamic environments.
Responsibilities:
Develop and implement state estimation and sensor fusion algorithms
Integrate multiple sensors into real-time navigation systems (IMU, GPS, LiDAR, Radar, Cameras, etc.)
Develop and improve control algorithms for stability and trajectory tracking
Analyze system and field logs to improve estimator and controller performance
Work on real-time, resource-constrained systems
Test and validate algorithms in simulation and real-world environments
Collaborate closely with Embedded, Perception, and Software teams
Write clean, maintainable, production-quality code
Requirements:
BSc or MSc in Mechanical Engineering, Aerospace Engineering, or related fields
3–5 years of hands-on experience with Kalman Filters, sensor fusion, and nonlinear dynamics
Experience with real-time systems and multi-sensor integration
Strong Python skills for analysis and tooling
Strong problem-solving and debugging capabilities
Ability to work independently in a fast-paced environment
Advantages:
Experience with autonomous robotics, UAV/UGV platforms
Experience with PX4, ROS2, or similar frameworks
Experience with C/C++ in Linux environments
Hands-on hardware integration, calibration, and field testing experience
Background in control theory (LQR, MPC, nonlinear control)
