Computer Vision & AI Engineer - Interceptor C‑UAS; m​/f​/d

At Quantum Systems, we are building highly autonomous interceptor systems that must perceive, decide, and act under extreme latency, motion, and environmental constraints.

We are looking for a Computer Vision & AI Engineer to drive the perception stack of our Counter-UAS interceptor platform. You will work on low-latency camera pipelines, small-object detection, object tracking, sensor fusion, visual odometry, precision landing, camera calibration, embedded inference, and data pipelines for training and validation.

This role is suited for someone with a strong academic or practical background in computer vision, AI, robotics, or perception systems. A Master's degree is expected; a PhD is a strong plus. We are open to ambitious early-career candidates if they bring strong technical depth, practical implementation skills, and the ability to own complex perception problems.

Your Mission

You will develop the vision and AI pipeline that turns raw camera data into actionable perception outputs for autonomous flight. This includes low-latency detection and tracking, motion-aware vision, sensor fusion with inertial data, 3D direction-vector estimation, GPS-denied navigation, and precision landing support.

• Bring up, optimize, and maintain high-performance camera pipelines, including CSI camera interfaces, raw image access, buffering, synchronization, and latency reduction.

• Develop detection algorithms for small and difficult-to-see objects in moving and rotating camera images.

• Combine machine learning and classical computer vision approaches where appropriate.

• Fuse inertial data, motion information, and visual data to improve detection and tracking in moving image sequences.

• Build object tracking pipelines that can switch from initial detection to low-latency tracking once a target has been acquired.

• Optimize perception pipelines for embedded execution on NVIDIA Jetson platforms.

• Work toward high frame-rate processing targets in the range of 100–300 FPS, where technically feasible.

• Use camera intrinsics and extrinsics to transform image-space detections into 3D direction vectors or other navigation-relevant outputs.

• Work on GPS-denied navigation concepts using visual odometry, including approaches with forward-facing camera views rather than only downward-looking cameras.

• Develop visual support for precision landing, including height estimation, velocity estimation, and motion-state estimation from limited camera perspectives.

• Build and maintain the data pipeline from onboard recordings to cloud storage, preprocessing, annotation, dataset generation, training, validation, and benchmarking.

• Work with annotation tools such as Super Annotate, CVAT, Label Studio, or comparable systems.

• Benchmark and evaluate different model and algorithm families, including approaches such as Center Net, Super Point, Super Glue, optical flow, feature tracking, object detection, and lightweight embedded models.

• Build deployment pipelines using ONNX, TensorRT, custom inference runners, or comparable embedded inference tooling.

• Collaborate closely with autonomy, flight control, embedded software, test, and systems engineering teams.

• Master's degree or PhD in computer vision, AI, robotics, machine learning, electrical engineering, computer science, aerospace engineering, or a comparable technical field.

• Strong understanding of computer vision fundamentals, camera geometry, feature detection, object detection, tracking, calibration, and image-space to 3D transformations.

• Practical experience implementing computer vision or machine learning pipelines in Python and C++.

• Experience with embedded inference, ideally on NVIDIA Jetson
  , CUDA, TensorRT, ONNX, GStreamer,…