Software Engineer (Perception)

Join Lodestar as a Software Engineer I - Perception, where you'll work on cutting-edge vision and perception models for autonomous decision-making in complex space environments. This early-career role offers real ownership and the opportunity to collaborate with senior engineers. Key responsibilities include developing and optimizing perception algorithms, integrating models into mission simulations, and maintaining perception tooling. Strong software engineering skills in Python and foundational knowledge in machine learning and computer vision are required.

• Contribuer au système de perception au cœur de la suite logicielle d'autonomie de Lodestar, en travaillant sur l'ensemble du cycle de vie des algorithmes de perception.
• Aider à développer et à tester nos méthodes pour prédire la pose relative des cibles spatiales dynamiques dans des environnements visuels difficiles.
• Collaborer avec les équipes d'autonomie, d'estimation et de logiciel de mission pour soutenir la fidélité, la performance et l'évolutivité de la perception.

• Solid software engineering skills in Python, and working proficiency in C++
• Foundational DNN/ML knowledge and hands-on experience with computer vision or image processing libraries (e.g. OpenCV)
• Demonstrated ability to take an ML model from training through to evaluation, in any setting — coursework, research, or industry
• 0–2 years of professional experience in machine learning, robotics, computer vision, or a related field. Internships, research placements, and substantial academic or personal projects all count
• Bachelor's or Master's degree in AI/ML, Computer Science, Robotics, a related field, or equivalent experience
• Experience developing or deploying ML models to real-time or resource-constrained systems
• Broad understanding of modern perception systems and their architecture
• Exposure to sensor measurements from RADAR, electro-optical sensors, RF sensors, or LIDAR
• Familiarity with image processing techniques (e.g. feature extraction, segmentation, filtering, stereo matching) to support 3D reconstruction and target classification
• Experience optimising neural networks or geometric algorithms using CUDA kernels, TensorRT, or other GPU acceleration frameworks
• Exposure to distributed training or cloud-based scaling of ML models (AWS, GCP, or Azure)
• Experience with Linux, Git, and CI/CD pipelines
• Understanding of networking and communication protocols for distributed autonomy systems
• Comfortable with containerisation tools such as Docker and Kubernetes