Perception & Fusion Engineer

Swarm Aero is redefining air power, building the largest swarming UAV and the most versatile swarming aircraft network in the world. The company is moving quickly to launch the first aircraft designed specifically for swarming, as well as the Command & Control software to mobilize swarms of thousands of heterogeneous autonomous assets and empower human operators to achieve superhuman results. The team has created and exited multiple startups, negotiated defense deals worth billions of dollars, and designed and built 30+ novel aircraft, with aerospace experience from Scaled Composites, Airbus, Archer Aviation, Blue Origin, and Boom Supersonic. About the Role We're looking for a Perception & Sensor Fusion Engineer to develop the algorithms that give the largest drone swarm in the world an accurate, shared picture of the battlespace. This is a deep algorithm role for someone who lives in the details of detection, tracking, and estimation, and wants to see their work fly. What You'll Do - Train, tune, and test automatic target recognition and track management systems using the latest advancements in neural networks.

• Develop multi-sensor fusion and state estimation algorithms (Kalman filters, particle filters, multi-target tracking) 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% of the time for onsite test and integration events.

• Demonstrated expertise in estimation theory and multi-target tracking (Kalman/particle filters, JPDA, MHT, random finite sets, or similar).
• Hands-on deep learning experience (training, evaluation, and deployment of neural networks for detection or classification).
• Strong programming skills in C++, Rust, Go, and/or Python.
• Track record of taking algorithms from research to deployment on a real system.

• Experience with EO/IR, radar, or RF sensor processing on aerial platforms.
• Experience deploying ML models to embedded or edge hardware (TensorRT, ONNX, quantization).
• Familiarity with defense sensing and tracking problem domains.

• Showcase a portfolio that includes at least one end‑to‑end perception pipeline (data preprocessing, model training, on‑device deployment) with performance metrics.
• Highlight any experience optimizing deep‑learning models for edge hardware; mention tools like TensorRT, ONNX, or quantization techniques.
• Prepare to discuss concrete examples where you moved an algorithm from research code to a production‑ready, real‑time system.
• If you have publications or open‑source contributions in sensor fusion or tracking, link them directly in your application.
• During interviews, expect white‑board problems on Kalman or particle filter derivations and coding tests in C++/Rust or Python.
• When negotiating, factor in equity and the unique mission impact; remote‑first roles often allow for flexible compensation structures.
• Be wary of any offer that does not provide clear details on remote work support or travel expectations, as on‑site test days are part of the role.