Lead SLAM CV Engineer

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October 14

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KRUSH Labs

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Hardware • Telecommunications • Cybersecurity

KRUSH Labs is a global, privately-owned research center founded in Eindhoven in 2025 that focuses on key research and product development for unmanned systems and hardware. The company develops advanced wireless communication platforms (SDR, MIMO, adaptive mesh networking), high-precision navigation and control systems (GNSS with RTK/PPP, SLAM, LiDAR, AI-enhanced VIO, MPC), embedded systems for edge processing, and integrated cybersecurity and data protection for mission-critical applications. KRUSH Labs collaborates with clients in electrical engineering and targets emerging markets that blend technology with agriculture, mobility, and communications, while also offering tailored software development and customer services.

11 - 50 employees

🔧 Hardware

📡 Telecommunications

🔒 Cybersecurity

📋 Description

• Lead the research, development and optimization SLAM algorithms tailored for unmanned systems. • Contribute to system architecture decisions, ensuring compliance with industry standards. • Contribute to technical documentation, code reviews, and best practices to ensure high-quality deliverables. • Mentor and manage a team of engineers, fostering technical growth and collaborative problem-solving. • Work closely with product managers, software engineers, and hardware teams to align SLAM development with project goals and timelines

🎯 Requirements

• PhD degree in Computer Science, Computer Vision, Machine learning, Robotics, Aerospace engineering or related technical field. • Extensive experience in SLAM algorithm development and deployment, with proven expertise in ORB SLAM2, ORB SLAM3, LSD SLAM or similar framework. • Deep knowledge of open source libraries (OpenCV) and perception based solutions. • Strong background in math, experience with state estimation. • Hands-on experience with C++ (preferably versions 17 or 20). • Managerial or team coordinating experience. • Familiarity with ROS (Robot Operating System) or other robotics middleware. • Familiarity with sensor Fusion, Kalman filters and other common signal processing algorithms.