Staff Engineer, Sensor Fusion & Localization

ARX Robotics

Munich, Germany

About The Role

At ARX Robotics, you will help advance the autonomy stack of next-generation unmanned ground vehicles. As a Staff Engineer – Sensor Fusion & Localization, you will develop core state estimation and sensor fusion systems that enable robots to understand their position and motion in complex, GPS-denied environments.

You will work across autonomy, perception, hardware, and software teams to integrate advanced algorithms into a cohesive and reliable platform for real-world deployment. Your contributions will directly influence how autonomous and teleoperated systems operate in demanding field conditions where precision, robustness, and low latency are critical.

What You’ll Build

Develop advanced sensor fusion and state estimation algorithms for robust localization in GPS-denied environments
Implement and improve methods such as EKF, UKF, particle filters, and nonlinear observers for state estimation and factor graph-based SLAM methods
Fuse and synchronize data from cameras, LiDAR, radar, IMUs, GNSS, magnetometers, and barometers into reliable real-time state estimates
Design low-latency, real-time processing pipelines for autonomous robotic systems
Build and refine localization performance through simulation, controlled testing, and real-world validation under degraded sensing conditions
Collaborate closely with hardware and software teams to ensure accurate calibration, synchronization, and system integration
Prototype, evaluate, and deploy new multi-sensor fusion and estimation approaches
Debug and resolve complex issues across sensor data, calibration, and estimation pipelines
Contribute to resilient GPS-denied navigation capabilities for autonomous ground vehicles

What You Bring

Advanced degree (MSc or higher) in Robotics, Computer Vision, Electrical Engineering, or a related field
Strong C++ development skills with experience debugging complex robotics systems
Deep expertise in sensor fusion and state estimation methods, including EKF, UKF, particle filters, and nonlinear estimation techniques
Solid understanding of inertial navigation and visual-inertial concepts
Hands-on experience integrating and calibrating multi-sensor systems (LiDAR, camera, IMU, GNSS, radar)
Strong foundation in linear algebra, probability theory, and 3D geometry
Experience working with ROS2 and simulation environments for algorithm development and validation
Familiarity with Linux-based development workflows, CI/CD pipelines, and modern robotics software practices
A structured, self-driven approach with strong problem-solving and debugging skills in complex systems
Experience with Python, CUDA or edge computing platforms are beneficial
Familiarity with ML frameworks such as PyTorch, TensorRT, or vision libraries like OpenCV are a plus