Deep Learning SLAM Engineer

About Luxolis:
Luxolis is at the forefront of building cutting-edge robotic systems with applications spanning autonomous navigation, augmented reality, and advanced perception.
We are a fast-growing company developing intelligent solutions that push the boundaries of computer vision, robotics, and deep learning.
Join us as we innovate and shape the future of autonomous systems.Position Overview:
We are seeking a highly skilled Deep Learning SLAM Engineer to join our dynamic team working on the development and deployment of state-of-the-art simultaneous localization and mapping (SLAM) algorithms that leverage deep learning.
As part of the Robotics & Computer Vision team, you will work on solving critical challenges related to robust environment perception, motion tracking, and spatial mapping in dynamic, real-world settings.
The ideal candidate will have strong expertise in both SLAM and deep learning techniques, along with a passion for robotics and autonomy.Key Responsibilities:
Develop and Implement SLAM Algorithms:
Design and optimize state-of-the-art SLAM algorithms that integrate deep learning models for robust and accurate localization and mapping in dynamic environments.Deep Learning Integration:
Apply deep learning techniques (e.g., CNNs, RNNs, transformers) to improve traditional SLAM systems for better feature extraction, object detection, and semantic understanding.Sensor Fusion:
Work with multimodal sensor data, including LiDAR, RGB cameras, depth sensors, and IMUs, to enhance SLAM performance under challenging conditions (e.g., low light, motion blur, moving objects).Algorithm Optimization:
Focus on optimizing SLAM systems for real-time performance, scalability, and resource efficiency (e.g., memory, processing power).Testing and Validation:
Design and run extensive tests to validate the robustness, accuracy, and real-world applicability of SLAM systems across different environments (urban, indoor, outdoor, etc.).Collaboration:
Work closely with cross-functional teams including hardware engineers, software developers, and data scientists to integrate SLAM algorithms into autonomous systems (robotics, drones, AR devices).Research & Innovation:
Stay up to date with the latest advancements in SLAM, deep learning, and robotics, and contribute to innovative solutions that drive the next generation of autonomous technologies.Documentation & Reporting:
Maintain clear documentation for algorithms, tests, and performance metrics, and report progress to the team and stakeholders.Qualifications:
Educational Background:
MSc or PhD in Computer Science, Robotics, Electrical Engineering, or a related field, with a focus on SLAM, computer vision, or machine learning.Technical Skills:
Strong knowledge of SLAM algorithms (e.g., ORB-SLAM, DSO, Visual-Inertial SLAM) and their real-world applications.Proficiency in deep learning frameworks (TensorFlow, PyTorch, Keras) and experience applying them to perception and localization tasks.Experience with sensor fusion techniques for integrating data from LiDAR, cameras, IMUs, etc.Familiarity with 3D reconstruction, point cloud processing, and structure-from-motion (SfM).Expertise in programming languages such as Python, C++, or ROS for algorithm development and integration.Experience:
3+ years of experience working with SLAM and/or deep learning in robotics or computer vision applications.Hands-on experience with autonomous systems, mobile robots, or drone navigation.Strong problem-solving skills and the ability to develop innovative solutions to challenging technical problems.Desirable:
Experience with reinforcement learning or optimization techniques for improving SLAM robustness.Familiarity with ROS (Robot Operating System) and tools for robot simulation and visualization (e.g., Gazebo, RViz).Previous experience with deployment of SLAM systems in real-world applications.