Principal GNC Engineer

About the company

Mach provides a full-stack autonomy platform that enables off-road equipment OEMs to deploy driverless, AI-powered machinery across agriculture, defense, landcare and other rugged industries.

About this role

ABOUT MACH INDUSTRIES Founded in 2023, Mach Industries is a rapidly growing defense technology company focused on developing next-generation autonomous defense platforms. At the core of our mission is the commitment to delivering scalable, decentralized defense systems that enhance the strategic capabilities of the United States and its allies. With a workforce of approximately 350 employees, we operate with startup agility and ambition. Our vision is to redefine the future of warfare through cutting-edge manufacturing, innovation at speed, and unwavering focus on national security. We are dedicated to solving the next generation of warfare with lethal systems that deter kinetic conflict and protect global security. THE ROLE Mach Industries is seeking a Principal GNC Engineer to define, develop, and flight-prove the GNC architecture for autonomous aircraft and related defense platforms. You will own system-level technical decisions across guidance, navigation, flight controls, modeling and simulation, embedded implementation, and flight test. This is a hands-on technical leadership role. You will solve the program’s hardest problems, establish engineering rigor, and guide multidisciplinary teams from early vehicle design through flight qualification and deployment. The ideal candidate combines deep flight-vehicle expertise with strong software judgment, decisive technical leadership, and a record of fielding reliable systems. KEY RESPONSIBILITIES - Own the end-to-end GNC architecture and technical roadmap across all mission phases, from launch or takeoff through terminal operations, recovery, or landing. - Translate mission objectives and vehicle concepts into GNC requirements, interfaces, performance budgets, and verification criteria. - Design, analyze, and mature guidance algorithms, control laws, state-estimation solutions, gain schedules, and control-allocation strategies. - Ensure robust performance across the full operating envelope while accounting for aerodynamic uncertainty, actuator limits, sensor errors, latency, structural modes, environmental disturbances, and off-nominal conditions. - Lead flight-dynamics modeling, system identification, Monte Carlo analysis, and model correlation using CFD, wind-tunnel, ground-test, and flight-test data. - Establish and maintain rigorous software-in-the-loop, and hardware-in-the-loop verification workflows. - Drive production implementation of flight-critical algorithms in embedded, real-time software; review code, numerics, timing, and fault-handling behavior. - Define flight-readiness criteria and lead GNC activities for test planning, safety reviews, range execution, data analysis, anomaly resolution, and rapid iteration. - Partner with aerodynamics, propulsion, avionics, embedded software, and test teams to resolve vehicle-level trades and integration risks. - Communicate technical decisions, performance margins, and program risks clearly to engineering leadership and program stakeholders. - Mentor engineers, lead design and code reviews, and establish reusable GNC methods, tools, and engineering standards. - Balance analytical rigor with program speed, making sound decisions with incomplete data and taking direct ownership of outcomes. REQUIRED QUALIFICATIONS - Bachelor’s degree in aerospace engineering, mechanical engineering, electrical engineering, robotics, physics, mathematics, or a related technical field. - Significant experience designing, implementing, and testing GNC systems for aircraft, missiles, launch vehicles, spacecraft, high-performance UAVs, or comparable autonomous systems, typically gained through 10 or more years of relevant work. - Demonstrated ownership of a GNC system through multiple phases of the development lifecycle, including requirements, analysis, implementation, verification, integration, and flight test. - Deep knowledge of flight dynamics, stability and control, classical and modern control theory, state estimation, trajectory generation, and system identification. - Experience developing robust algorithms for nonlinear systems with real-world actuator, sensor, computational, and timing constraints. - Proficiency with MATLAB/Simulink for modeling and analysis, plus C or modern C++ for production or embedded implementations. - Experience with Monte Carlo analysis, uncertainty quantification, requirements-based verification, and SIL/HIL test environments. - Strong understanding of flight-test practices, telemetry analysis, controller tuning, anomaly investigation, and risk-managed test progression. - Fluency with modern engineering workflows, including Git, code review, continuous integration, issue tracking, requirements traceability, and configuration management. - Proven ability to lead complex technical decisions, communicate across disciplines, and mentor other engineers without relying on formal authority. PREFERRED QUALIFICATIONS - Master’s degree or Ph.D. in aerospace engineering, controls, robotics, or a related field. - Experience fielding autonomous fixed-wing aircraft or high-performance unmanned systems. - Expertise in one or more advanced areas such as nonlinear or adaptive control, model-predictive control, LQR/LQG/LTR, robust control, or control allocation for over-actuated vehicles. - Experience with tightly coupled GNSS/INS navigation, multi-sensor fusion, terrain- or vision-aided navigation, or operation in degraded or denied environments. - Experience integrating seekers, radar, EO/IR, datalinks, or mission-autonomy systems into guidance and navigation architectures. - Familiarity with PX4, ROS 2, real-time operating systems, and resource-constrained embedded processors. - Experience with DO-178C or equivalent safety-critical development processes. - Experience building reusable simulation frameworks, digital twins, automated regression infrastructure, or model-based systems engineering workflows. Di