Flight Software Manager

Reliable Robotics is building safety-enhancing automated aviation technology. The Flight Software team builds safety critical embedded flight software, next generation detect and avoid and communications technology, FPGA designs, and simulation and test infrastructure, and is deeply involved in the development and certification of the Reliable Robotics autonomy stack. This role is located onsite at the HQ in Mountain View, CA and requires willingness to travel 10% of the time. The position requires access to information subject to U.S. export controls.

Responsibilities

Lead and grow the team responsible for safety critical flight software; Own the maturation of engineering processes to balance immediate engineering execution with formal DO-178C verification; Support rapid integration and testing of GNC algorithms while building out the software architecture for certification; Provide technical and architectural guidance on flight software components, from flight controls to redundancy management; Work closely with leads across GNC, Avionics, Systems, and Hardware to develop and execute technical roadmaps, manage priorities, and ensure the team is unblocked and empowered to deliver robust, reliable, and certifiable flight software

Requirements

Bachelor's Degree in Aerospace, Computer Science, or related discipline; 7+ years of professional experience in real-time embedded software development (C/C++); 3+ years in a technical leadership or people management role; Experience working on complex systems where hardware and software intersect; Experience with Guidance, Navigation, and Control (GNC) algorithms, flight dynamics, and state estimation

Nice to have

Demonstrated success scaling teams and maturing development processes in a high-growth environment; Experience designing and implementing fault-tolerant systems and redundancy management schemes; Experience working through the full lifecycle of a DO-178C certification program; Professional experience with Python for tooling, test, and data analysis