Perception and Fusion Engineer

This full-time position involves developing algorithms that provide a shared, accurate view of the operational environment for a large-scale drone swarm. Day-to-day tasks include training and testing automatic target recognition systems using neural networks, as well as creating multi-sensor fusion and state estimation methods for resource-constrained onboard hardware. The engineer will also design distributed approaches to merge data from multiple vehicles into a single coherent picture and optimize these models for real-time inference on edge computing devices. Additionally, the role requires rigorous performance characterization against flight data and simulation, along with writing efficient, maintainable code. Candidates must possess a Master’s or PhD in Robotics, Computer Science, Electrical Engineering, Applied Math, or a related field, or demonstrate equivalent applied experience. Specific technical expertise in estimation theory and multi-target tracking is required, with familiarity in methods such as Kalman filters, particle filters, JPDA, MHT, or random finite sets. The job is based in a remote working arrangement, though it includes up to 25% travel for onsite testing and integration events. This deep algorithmic role suits individuals who focus on the details of detection, tracking, and estimation and desire to see their work deployed in flight.