Principal Engineer, State Estimation (R4602)

United StatesFull Time Employeelead

OtherEngineerPrincipal Engineer

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Requirements Summary

Design and implement Extended Kalman Filter algorithms for navigation applications, with broad understanding of state estimation theory and alternative

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OtherEngineerPrincipal Engineer

Founded in 2015, Shield AI is a venture-backed deep-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include the V-BAT and X-BAT aircraft, Hivemind Enterprise, and the Hivemind Vision product lines. With offices and facilities across the U.S., Europe, the Middle East, and the Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.

We are looking for an experienced state estimation engineer to design, develop, and support the deployment of safety-critical navigation solutions for aerospace platforms. This role requires deep expertise in state estimation theory, extensive practical experience implementing ownship navigation systems in the aerospace domain, and strong software development skills for production-quality navigation code. The engineer will design and implement a unified navigation architecture that supports traditional GNSS/IMU fusion while being extensible to all-source navigation strategies incorporating diverse sensing modalities for GPS-degraded and denied environments.

Navigation System Architecture & Design:

Establish navigation performance requirements and error budgets for safety-critical applications

Support decomposition of navigation requirements into allocations for sensors, estimation algorithms, and software components

Design detailed software architecture for state estimation implementations, including module interfaces and data flow

Algorithm Development & Implementation:

Design and implement Extended Kalman Filter algorithms for navigation applications, with broad understanding of state estimation theory and alternative filtering approaches

Implement tightly-coupled and loosely-coupled GNSS/INS integration algorithms

Integrate diverse sensing modalities (vision, RF, celestial etc.) into multi-sensor fusion framework for GPS-degraded environments

Develop fault detection, isolation, and recovery (FDIR) strategies for navigation systems

Implement integrity monitoring and protection level calculations for safety-critical operations

Certification & Verification:

Develop verification and validation test plans for navigation algorithms

onduct performance analysis including Monte Carlo simulation, covariance analysis, and flight test data evaluation

Document navigation system design, requirements allocation, and compliance evidence

Support safety assessment activities including failure modes and effects analysis

Technical Leadership:

Provide technical guidance on navigation architecture and state estimation approaches

Support trade studies evaluating navigation sensor suites and fusion strategies

Mentor junior engineers on state estimation theory and implementation

MS or PhD in Computer Science, Software Engineering, Electrical Engineering, Aerospace Engineering, Mechanical Engineering, Applied Mathematics, or related field
15+ years of experience developing state estimation algorithms for aerospace navigation applications

Demonstrated experience implementing GNSS/INS integration solutions

Experience with safety-critical system development and certification processes

Deep understanding of state estimation theory

Experience implementing multi-sensor fusion algorithms in production systems

Strong background in inertial navigation, GNSS positioning, and sensor error modeling

Strong programming skills in C/C++ and Python/MATLAB for algorithm development and analysis

Understanding of integrity monitoring, protection levels, and safety assessment methods

Experience with requirements management and verification/validation processes for certifiable systems

Understanding of GPS/GNSS signal structure, error sources, and performance characteristics

Knowledge of IMU error models, calibration, and Allan variance analysis

Familiarity with alternative navigation sensors (camera, RF ranging, celestial, etc.)

Understanding of navigation performance metrics (accuracy, integrity, continuity, availability)