Autonomy Lead

Skyways designs, builds, and operates fully autonomous long-range cargo aircraft. Founded in 2017, the company has spent the last eight years developing and deploying autonomous logistics systems for real-world operations.

Our V2 aircraft carries 30 lbs up to 500 miles, and V3 has a maximum range of well over 1,000 miles, a maximum useful load of 100 lbs, and a maximum endurance of nearly 20 hours. Both use a hybrid-electric architecture that takes off like a helicopter and cruises like a plane, enabling long-range autonomous delivery without traditional runway infrastructure.

Today, Skyways aircraft operate across three continents in controlled national airspace under FAA oversight and in support of U.S. military operations. We are transitioning from prototype development to full-rate production and scaling toward large autonomous cargo fleets.

Backed by Y Combinator and a $37M AFWERX STRATFI award from the U.S. Air Force, Skyways is creating a new form of transportation to advance our civilization from Austin, Texas.

The Opportunity This role owns how autonomy works in real-world flight, and it owns the small team that builds it.

You will define how perception, state estimation, planning, and flight behaviors come together into a system that actually completes missions reliably. This is not about building isolated components. It is about making the full stack work under real constraints, and about leading a team of autonomy engineers to do the same. You will shape how our ML systems improve with real-world data and ensure they perform outside of ideal conditions. You will lead technical direction for autonomy while remaining deeply hands-on in architecture, implementation, testing, and flight debugging. Your work directly impacts mission success, aircraft reliability, and operational deployment at fleet scale. We are looking for someone who has already built and deployed autonomy systems on real-world robotic or UAV platforms, and who wants to grow a team while staying close to the code.

Own the end-to-end autonomy stack across perception, estimation, planning, and system behavior Lead, manage, and grow a team of autonomy engineers, including hiring, onboarding, performance, and development Be accountable for your team's output, technical standards, morale, and retention Define system architecture and make tradeoffs across reliability, performance, and operational complexity Set technical direction for autonomy while remaining hands-on in critical technical areas Drive integration across autonomy, GNC, mission planning, and aircraft systems Own how autonomy is validated across simulation, ground testing, and flight operations Use flight data, telemetry, and operational feedback to identify failures and improve system performance Drive how datasets, evaluation pipelines, and iteration loops improve deployed autonomy systems Debug real-world autonomy failures and close gaps between controlled testing and operational deployment Work closely with flight operations and hardware teams to improve reliability in real environments

Experience building and deploying autonomy systems on real robotic or UAV platforms Experience leading and developing engineers, including direct people management or a clear track toward it Strong system-level understanding across perception, estimation, and planning, with depth in at least one area Experience taking autonomy systems from development through operational deployment and iteration Strong proficiency in C++ and Python Experience debugging real-world autonomy failures using logs, telemetry, and system-level analysis Experience working across software, hardware, and flight or field operations Ability to make practical engineering tradeoffs under real operational constraints Experience improving ML or perception systems using real-world operational data

Experience with autonomous aircraft or aerial systems Background in robotics, computer vision, or motion planning Experience with real-time or embedded systems Familiarity with ROS2 or similar robotics frameworks Experience deploying autonomy or ML systems to resource-constrained environments

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