Computer Vision Engineer

InWARVVRHLP

Apply for this position and join us in building Europe’s next generation infrastructure.

Our values are rooted in responsibility, readiness, and long term thinking. We believe sovereignty must be designed into systems from the start. Readiness is achieved through capability, not procurement. Autonomy is infrastructure, not a feature. We build with the understanding that modern systems carry long term consequences. That is why we prioritise reliability over novelty, integration over isolation, and sustained capability over short term advantage.

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Position

Role Overview

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Develop and optimize perception algorithms for UAV platforms. Focuses on real-time detection, classification,

tracking, vision-based pose estimation, EO/IR sensor fusion, and edge deployment under strict latency, power, and

memory constraints in safety-critical, defense-grade environments.

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Key Responsibilities

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Computer Vision & Perception

• Design and implement real-time object detection, classification, multi-target tracking, and change detection for
  
  

autonomous UAV operations, including high-altitude ground target detection, camouflage, and foliage

occlusion.

• Optimize deep learning models (YOLO, transformers, CNNs, or proprietary architectures) for edge deployment
  
  

on embedded platforms (NVIDIA Jetson, Qualcomm RB5) under strict latency, power, and memory budgets.

• Develop vision-based pose estimation, optical flow, and feature tracking pipelines for mission-critical
  
  

applications.

• Implement camera calibration routines and multi-camera rig setups for stereo and wide-baseline
  
  

configurations.

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Sensor Fusion For Perception

• Develop sensor fusion pipelines combining EO/IR (thermal/FLIR), depth, and RF data to produce robust
  
  

environment representations across day/night and degraded conditions.

• Implement probabilistic fusion approaches (complementary filters, tightly-coupled vision-IMU) to maintain
  
  

perception reliability under low light, motion blur, and occlusion.

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Integration & Validation

• Integrate perception pipelines with ROS2-based software stacks and companion computers (Jetson, RB5).
• Benchmark algorithms in simulation (Gazebo, MATLAB/Simulink) and HIL/HITL testbeds; validate through
  
  

field trials.

• Measure and report KPIs (detection accuracy, latency, false-positive rate, robustness under environmental
  
  

variation) and iterate on improvements.

• Document algorithm designs, performance characteristics, and integration interfaces; support certification efforts.
  
  

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Experience & Skills

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Required Qualifications & Experience

• Master's or PhD in Computer Science, Electrical Engineering, Robotics, or related field.
• 3+ years developing computer vision algorithms for real-world autonomous systems (UAVs, robotics, or
  
  

autonomous vehicles).

• Proven expertise in object detection, tracking, feature extraction, visual odometry, and camera calibration.
• Proficiency in C++ and C (OpenCV, Eigen) and Python (NumPy, SciPy, scikit-learn, scikit-image).
• Hands-on experience with detection and segmentation frameworks (YOLOv8+, Detectron2, MMDetection,
  
  

Mask R-CNN, U-Net).

• Advanced use of PyTorch, TensorFlow/Keras, and ONNX for model training and export.
• Experience deploying and optimizing models on embedded platforms (Jetson, Qualcomm RB5, ARM).
• Experience with EO/IR sensor stacks: thermal (FLIR/uncooled) camera integration, NUC calibration, and
  
  

thermal-domain object detection.

• Strong mathematical foundation in linear algebra, probability theory, and optimization.
• Solid version control practices (Git/GitLab) and dataset/model management (DVC or equivalent).
• English: Upper Intermediate or higher.
• Good communication skills and ability to cooperate with adjacent engineering teams.
• National from a NATO member country or one of the following NATO Indo-Pacific partners: Australia, Japan,
  
  

South Korea, New Zealand or Ukraine.

• Free criminal record
  
  

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Preferred Qualifications & Experience

• Proficiency in ROS2: node development, sensor integration, message pipelines.
• Experience with edge AI optimization: quantization, pruning, model compression for TensorRT, ONNX
  
  

Runtime, OpenVINO.

• Familiarity with annotation platforms (Label Studio, Labelbox) and experiment tracking tools (MLflow,
  
  

Neptune.ai, TensorBoard).

• Knowledge of tightly-coupled vision-IMU fusion (VINS-Mono, Kimera) for perception-side pose estimation.
• Familiarity with safety-critical standards: DO-178C (avionics software), MISRA C/C++ coding guidelines,
  
  

STANAG 4671 (UAV airworthiness) as applied to perception pipelines used in flight-critical decisions.

• Track record of publication at CVPR, ICRA, IROS, or IEEE Transactions, or contributions to open-source CV repositories.
  
  

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