eVTOL Autonomy Engineer

About the role

You will join an eVTOL aircraft company to build the autonomy functions that sit above the flight control laws: detect and avoid, trajectory planning, and the perception stack that keeps the aircraft separated from traffic and obstacles. This team owns the higher-level decision making that will let the aircraft operate safely in dense urban airspace. Your work bridges robotics research and certifiable aviation software, two worlds with very different standards of proof. You will collaborate with systems engineers on the safety case, software engineers on implementation, and flight test on validation, spanning the full path from algorithm to certified function.

What you will achieve

• Design and validate a detect-and-avoid function that meets target levels of safety against cooperative and non-cooperative traffic in representative encounters.
• Build trajectory planning algorithms that generate flyable, deconflicted paths through urban corridors in real time on flight hardware.
• Integrate radar, LiDAR, and vision sensors into a fused perception picture, characterizing detection performance across weather, lighting, and clutter conditions.
• Prove autonomy behavior in simulation at a scale flight test cannot match, running millions of encounters to bound the edge cases before flight.
• Deliver the autonomy verification evidence, from simulation results to flight test correlation, that feeds the certification safety case. Statistical rigor quantifies performance across the encounter space, including the rare edge cases the safety case depends on.

What you will bring

Must-haves

• 2 to 5 years developing autonomy, perception, or planning software for aircraft, drones, or autonomous vehicles with real-world deployment.
• Strong background in sensor fusion, probabilistic state estimation, or multi-object tracking under uncertainty.
• Experience with path planning or decision-making algorithms that run under hard real-time constraints on embedded targets.
• Proficiency in C++ and Python, with software that has flown or driven on real hardware, not just in simulation.
• Understanding of aviation surveillance concepts such as ADS-B, TCAS logic, or emerging detect-and-avoid standards.
• Ability to design simulation-based validation campaigns and interpret their statistical results honestly, including the tails.

Nice-to-haves

• Familiarity with DO-178C and the challenges of certifying autonomy functions to design assurance levels.
• Experience with formal methods or runtime assurance architectures that bound autonomy behavior.
• Background in human factors for pilot-autonomy interaction and handover design.
• Knowledge of urban airspace concepts and UAM traffic management frameworks.