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machine learning engineer jobs near münchen, by

Computer Vision & AI Engineer - Interceptor C‑UAS (m/f/d)

Quantum-Systems GmbH -
Gilching

Direkt bewerben

Details zur Stelle

Vollzeit
vor 12 Tagen

Qualifikationen

Doktorat
Forschungserfahrung
Master
C++
C
Bachelor
Machine Learning
Systems Engineering
GitHub
Künstliche Intelligenz
Robotik
Python

Vollständige Stellenbeschreibung

About the Role

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.

What is your Day to Day Mission:

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 SuperAnnotate, CVAT, Label Studio, or comparable systems.
Benchmark and evaluate different model and algorithm families, including approaches such as CenterNet, SuperPoint, SuperGlue, 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.

What You Bring to the Team

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, V4L2, or similar technologies.
Ability to read, understand, and implement ideas from current research papers.
Understanding of latency, throughput, profiling, memory movement, and real-time constraints.
Experience with dataset creation, annotation workflows, training/validation splits, metrics, and benchmarking.
Strong mathematical intuition and willingness to debug both algorithms and real-world sensor data.
Ability to take ownership of a technical area and drive it from research prototype to flight-test-ready software.
- Experience with UAV perception, robotics perception, visual odometry, SLAM, sensor fusion, or tracking systems.
- Experience with IMU-camera fusion, ego-motion compensation, rolling-shutter effects, or high-frame-rate cameras.
- Experience with precision landing, visual navigation, or GPS-denied navigation.
- Experience building cloud-based ML training and validation pipelines.
- Publications, thesis work, GitHub projects, demos, or competition results in computer vision, robotics, AI, or autonomous systems.

Why Join Quantum-Systems?

Be at the forefront of next-generation Defence innovation.
Work in a fast-paced, agile environment where your ideas make an impact.
Collaborate with a team of industry pioneers who are ambitious, bold, and visionary.
Opportunities for individual and professional growth in a globally recognized organization.

About us:

Quantum Systems specializes in the development, design, and production of small Unmanned Aerial Systems (sUAS). The company’s range of electric vertical take-off and landing (eVTOL) sUAS are built to maximize range and versatility and to provide operators with a seamless user experience. By integrating cutting-edge software capabilities, like edge computing and real-time AI-powered data processing, Quantum Systems is building next-generation UAS for clients in the defense, security, and public sectors.

How to Apply

Please include as your cover letter:

A detailed description of your hands-on projects, including photos, GitHub links, and videos, drawings.

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