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

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.