Quantum-Systems GmbH is hiring a Computer Vision & AI Engineer for its N3XT Interceptor C-UAS platform in Gilching, Germany. This is a highly technical, defense-adjacent role focused on autonomous drone interception systems. Your resume needs to speak the language of perception engineering, embedded AI, and real-time robotics from the very first line.
Understand What This Role Actually Demands
This is not a standard machine learning position. Quantum-Systems is building systems that perceive, decide, and act under extreme latency and environmental constraints. The job sits at the intersection of computer vision, embedded systems, and autonomous flight.
Before writing a single word on your resume, read the job description carefully. The company explicitly mentions low-latency camera pipelines, small-object detection, sensor fusion, and NVIDIA Jetson optimization. Those exact phrases need to appear in your resume if they reflect your real experience.
Lead With a Targeted Summary Statement
Your resume summary is the first thing a recruiter reads. Make it count. Skip vague phrases like "passionate engineer" or "team player with strong communication skills." Those say nothing useful.
Instead, open with something specific. Mention your years of experience in computer vision or perception engineering. Name the platforms, frameworks, or domains you have worked in directly.
A strong summary for this role might highlight your background in real-time vision systems, embedded inference, or autonomous navigation. Keep it to three or four lines. Precision beats length every time.
What Technical Skills to Highlight
Quantum-Systems wants depth across several interconnected areas. Your skills section should reflect that range without becoming a random list of buzzwords. Group your skills logically.
Relevant technical areas to feature include:
- Computer vision frameworks: OpenCV, PyTorch, TensorFlow, TensorRT
- Embedded platforms: NVIDIA Jetson (Nano, Xavier, Orin), CUDA optimization
- Detection and tracking: YOLO variants, ByteTrack, DeepSORT, Kalman filtering
- Sensor fusion: IMU integration, visual-inertial odometry, EKF/UKF
- Camera systems: CSI interfaces, raw image access, camera calibration (Zhang method, ROS camera_calibration)
- Navigation: GPS-denied navigation, visual odometry, precision landing algorithms
- Programming languages: C++, Python, CUDA C
Only list skills you can defend in a technical interview. Quantum-Systems runs a serious engineering operation. Recruiters there will probe deep into any claim you make.
How to Frame Your Work Experience
Experience entries are where most candidates lose the opportunity. Listing job duties is not enough. Recruiters want to see what you built, what performance you achieved, and what constraints you worked under.
For each relevant role, structure your bullet points around outcomes. Use numbers wherever possible. Latency figures, frame rates, detection accuracy metrics, and model compression ratios all tell a concrete story.
Examples of strong bullet point framing:
- Reduced end-to-end detection latency from 45ms to 12ms on Jetson Xavier through TensorRT INT8 quantization
- Developed a small-object detector achieving 87% mAP on aerial drone datasets using YOLOv8 with custom anchor tuning
- Implemented visual-inertial odometry pipeline for GPS-denied indoor navigation with sub-10cm position drift over 50m
- Built CSI camera pipeline with zero-copy buffer management, achieving consistent 60fps raw image capture on embedded hardware
Each of those bullets reflects something the job description directly calls out. That alignment is what gets your resume through both software filters and human review.
Academic Background and How to Present It
Quantum-Systems expects a Master's degree and considers a PhD a strong plus. If you hold either, feature it clearly near the top of your resume, not buried at the bottom.
For Master's and PhD holders, include your thesis topic if it is relevant. A thesis on visual odometry, autonomous UAV navigation, or real-time object detection directly signals technical alignment. Mention key publications or conference papers if they relate to perception systems or embedded AI.
Early-career candidates without a PhD still have a path here. The company says it is open to ambitious candidates with strong technical depth and practical implementation skills. In that case, your projects, open-source contributions, and competition results carry extra weight.
Projects and Portfolio Work That Stands Out
A dedicated projects section can be powerful for this role. Quantum-Systems values people who can own complex perception problems independently. Showing self-directed technical work demonstrates exactly that quality.
Strong project examples to include:
- Custom drone detection datasets built from scratch and used to train edge-deployed models
- Open-source tracking pipelines with documented benchmarks on public datasets
- Robotics competition entries involving autonomous perception and navigation
- Contributions to ROS2 packages, OpenCV, or perception-related open-source repositories
Include GitHub links where relevant. Engineers reviewing your resume will often check repositories directly before an interview. Clean, documented code leaves a strong impression.
ATS Tips for This Specific Role
Most large companies use Applicant Tracking Systems to filter resumes before a human ever sees them. Quantum-Systems likely uses one too, even for highly technical roles. Passing the ATS filter is a prerequisite.
Use these strategies to improve your ATS score:
- Mirror the exact language from the job description where accurate. Use "camera calibration," "sensor fusion," and "embedded inference" verbatim
- Avoid tables, graphics, text boxes, and multi-column layouts. ATS parsers often misread them
- Save your resume as a plain PDF or Word document. Avoid heavily designed templates
- Spell out acronyms at least once. Write "Counter-UAS (C-UAS)" rather than only the abbreviation
- Use standard section headers like "Experience," "Education," and "Skills." Clever alternatives confuse parsers
Keyword density matters, but do not stuff your resume. Weave relevant terms naturally into your experience descriptions and summary.
What Recruiters at Quantum-Systems Look For
Quantum-Systems operates in the defense and autonomous systems space. Recruiters there are looking for engineers who can handle real-world complexity, not just benchmark performance. Clean lab results are table stakes. Deployment on constrained hardware under motion, noise, and lighting variability is what matters here.
They also look for ownership mentality. The job description uses phrases like "own complex perception problems" and "drive the perception stack." Passive contributors do not fit. Your resume should reflect instances where you led technical decisions, not just executed tasks assigned to you.
Interdisciplinary range also matters. This role blends classical computer vision with deep learning and embedded systems. Candidates who show strength across all three areas are far more compelling than specialists in only one.
Formatting and Length Recommendations
Keep your resume to one or two pages maximum. For candidates with under six years of experience, one page is appropriate. Senior engineers with extensive publication and project histories may justify two pages.
Use a clean, readable font like Calibri, Arial, or Garamond at 10.5 to 12 point size. Maintain consistent spacing and alignment throughout. Recruiters reviewing dozens of applications notice sloppy formatting immediately.
Prioritize relevance over completeness. Remove internships or roles that have no connection to computer vision, robotics, or embedded systems. Every line on your resume should earn its place by reinforcing your fit for this specific role.
Apply for the Role
Quantum-Systems GmbH is building technology at the frontier of autonomous defense systems. The Computer Vision & AI Engineer position on the N3XT Interceptor C-UAS team in Gilching offers rare exposure to perception engineering at production scale under real operational constraints. Candidates with strong technical foundations in embedded vision and real-time detection should apply directly at this link.
