State Estimation & Navigation Engineer

We are seeking a talented State Estimation & Navigation Engineer to join a growing defense technology company developing next-generation autonomous systems.

This is an exciting opportunity to work on cutting-edge autonomous vehicles designed to operate in highly dynamic, challenging environments. The ideal candidate will have a strong background in robotics, aerospace engineering, state estimation, sensor fusion, and navigation algorithms, with the ability to develop and implement solutions for real-world autonomous platforms.

This position offers the opportunity to take ownership of navigation and state estimation architecture, working closely with multidisciplinary engineering teams to develop advanced autonomous systems from initial design through testing and deployment.

Key Responsibilities

• Design, develop, and implement state estimation and navigation algorithms for high-performance autonomous vehicles.
• Develop robust sensor fusion solutions integrating IMUs, GNSS, vision systems, and other onboard sensors.
• Build and optimize navigation algorithms for GPS-denied and GPS-contested environments.
• Implement advanced estimation techniques, including Kalman Filters, Extended Kalman Filters (EKF), Unscented Kalman Filters (UKF), and other nonlinear estimation methods.
• Develop algorithms for real-time vehicle position, velocity, orientation, and target-relative state estimation.
• Design and implement efficient software solutions using C++ and Python for embedded, resource-constrained systems.
• Collaborate with guidance, navigation, control, robotics, and software engineering teams to integrate estimation algorithms into autonomous platforms.
• Analyze sensor performance, uncertainty, observability, and estimation accuracy across different operating conditions.
• Develop simulation environments and conduct software-in-the-loop (SITL) and hardware-in-the-loop (HITL) testing.
• Validate algorithm performance through simulation, hardware testing, and real-world flight experiments.
• Troubleshoot navigation performance issues and continuously improve system accuracy, reliability, and robustness.

Required Qualifications

• Master's degree or PhD in Aerospace Engineering, Robotics, Electrical Engineering, Mechanical Engineering, Computer Science, or a related technical field.
• Strong understanding of state estimation, sensor fusion, and navigation algorithms.
• Experience with Kalman filtering, EKF, UKF, or other probabilistic estimation techniques.
• Proficiency in C++ and Python.
• Experience developing algorithms for autonomous vehicles, robotics, UAVs, or related systems.
• Strong foundation in linear algebra, probability, dynamics, optimization, and control theory.
• Familiarity with inertial measurement units (IMUs), GNSS, and other navigation sensors.
• Experience developing, testing, and validating algorithms through simulation or physical autonomous systems.
• Ability to work independently and take ownership of complex technical challenges.

Preferred Qualifications

• PhD with research focused on state estimation, autonomous navigation, robotics, or guidance, navigation, and control (GNC).
• Experience developing GPS-denied or GPS-contested navigation solutions.
• Experience with visual-inertial odometry (VIO), SLAM, factor graphs, or nonlinear optimization.
• Experience with high-speed autonomous vehicles, UAVs, aerospace platforms, or defense systems.
• Familiarity with embedded systems, real-time computing, and low-size, weight, and power (SWaP) constraints.
• Experience integrating IMU, GNSS, camera, and other sensor data into navigation systems.
• Experience with PX4, ROS/ROS 2, MATLAB/Simulink, or similar robotics and simulation frameworks.
• Experience with SITL, HITL, and real-world flight testing.
• Background in target tracking, target-relative navigation, or multi-sensor estimation.

Candidates with strong academic research backgrounds are encouraged to apply. Extensive industry experience is not required for individuals who demonstrate relevant technical expertise through graduate research, laboratory work, or hands-on autonomous systems projects.