Swarm Perception & Sensor Fusion Engineer

Swarmaero; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Swarm Aero seeks a Perception & Sensor Fusion Engineer to develop algorithms that give the world's largest drone swarm a shared, accurate view of the battlespace. You will focus on detection, tracking, and estimation, delivering end-to-end onboard fusion and real-time inference on edge compute.

Responsibilities include training recognition systems, implementing multi-sensor fusion, and validating performance with real flight data.

Qualifications

• MS or PhD in Robotics, Computer Science, Electrical Engineering, Applied Math, or related field, or equivalent depth of applied experience.
• Demonstrated expertise in estimation theory and multi-target tracking (Kalman/particle filters, JPDA, MHT, random finite sets, or similar).
• Hands-on deep learning experience with sensor fusion and real‑time inference.

Responsibilities

• Train, tune, and test automatic target recognition and track management systems using neural networks.
• Develop multi-sensor fusion and state estimation algorithms running onboard resource-constrained vehicles.
• Design distributed fusion approaches that combine tracks across the swarm into a single coherent picture.
• Rigorously characterize algorithm performance against real-world flight data and simulation.
• Optimize models and estimators for real-time inference on edge compute.
• Write clean, maintainable, and efficient code.
• Travel up to 25% for onsite test and integration events.

Skills

Estimation theory

Multi-target tracking

Neural networks

Deep learning

Education

MS/PhD in Robotics or related field

Tools

Kalman filters

Particle filters

Job description

Swarm Aero seeks a Perception & Sensor Fusion Engineer to develop algorithms that give the world's largest drone swarm a shared, accurate view of the battlespace. You will focus on detection, tracking, and estimation, delivering end-to-end onboard fusion and real-time inference on edge compute.

Responsibilities include training recognition systems, implementing multi-sensor fusion, and validating performance with real flight data.