Swarm Robotics Engineer
The job description
Tech stack. Multi-agent algorithms, distributed control, ROS 2, mesh networking, formation control, task allocation, simulation (Gazebo), Python/C++, consensus protocols
About the role
You will join a drone technology company building fleets that operate as one: multiple aircraft coordinating search patterns, mapping missions, or synchronized shows with minimal human supervision. You design the distributed algorithms and communication architectures that let vehicles share information and divide work intelligently. One drone is a product; a coordinated swarm is a capability, and you are the engineer who builds that capability from math to flight. You will also design the operator interface concepts for swarm supervision, ensuring a single operator can task, monitor, and intervene across the fleet without being overwhelmed by information during fast-moving missions. Your work will be demonstrated live to customers and partners, where reliability under real-world conditions matters more than laboratory performance.
What you will achieve
- Design and implement multi-vehicle coordination behaviors, including formation flight, distributed search, and area coverage, that work reliably in real flight and not just in simulation.
- Scale swarm operations by building task allocation and deconfliction logic that handles growing fleet sizes without operator workload growing alongside them.
- Improve swarm robustness by designing graceful degradation: the mission continues safely and productively when vehicles drop out or communication links degrade.
- Validate swarm behaviors in high-fidelity simulation before flight, catching coordination failures early when they are cheap to fix.
- Demonstrate swarm capabilities in flight tests and customer demonstrations that prove the technology is ready for operational use.
What you will bring
Must-haves
- 2-5 years of experience in multi-robot systems, distributed control, or swarm robotics with demonstrations on real hardware.
- Strong background in multi-agent algorithms: consensus, formation control, task allocation, or distributed planning.
- Proficiency in ROS 2 and simulation tools such as Gazebo for multi-vehicle scenario development.
- Understanding of the networking realities of swarms: latency, bandwidth limits, packet loss, and mesh topology behavior.
- Solid C++ and Python skills for both real-time implementation and rapid prototyping.
- Experience taking multi-vehicle systems from simulation through to fielded flight tests.
- Bachelor's or Master's degree in robotics, computer science, or engineering.
Nice-to-haves
- Experience with drone light shows or other large-scale coordinated flight events.
- Background in game theory or optimization applied to multi-agent tasking problems.
- Familiarity with UTM concepts and multi-vehicle airspace deconfliction.
- Experience with decentralized decision-making under intermittent connectivity.
Anduril
Skydio
Shield AI
Zipline
DJI
AeroVironment