Perceptive Space Systems is building a decision intelligence platform to help satellite and launch operators navigate the growing risks posed by space weather and the space environment. We work at the intersection of aerospace, AI, and real-time systems, combining cutting-edge modeling, sensor fusion, and autonomy to improve operational resilience in orbit. Read more here.

MINIMUM QUALIFICATIONS

• Strong proficiency in state estimation techniques, including Kalman filtering (EKF, UKF) and batch filtering.
• Expertise in non-linear optimization and computational mathematics.
• Proficient in Python AND C/C++.
• Experience working with GNSS, radar, lidar, and camera data, including sensor calibration and error modeling in real-world applications/projects.
• Experience with modern software engineering practices: version control (Git), CI/CD, cloud-based workflows, and peer review.
• 4+ years of industry experience with a Master’s or PhD in Aerospace Engineering, Electrical Engineering, Mechatronics, or a related field.
• Excellent communication and collaboration skills; able to work effectively across disciplines.
• Experience working in a fast-paced, process-oriented, high-ownership, and demanding startup environment.

PREFERRED QUALIFICATIONS

• Experience with uncertainty quantification and Monte Carlo methods.
• Exposure to machine learning frameworks (e.g., PyTorch, TensorFlow).
• Experience with spacecraft guidance, navigation, and control (GNC) or orbit determination.
• Experience with Rust or Go.

• Design and implement probabilistic filtering techniques (e.g., Kalman, Extended Kalman, Particle Filters) for robust real-time state estimation and uncertainty modeling.
• Develop sensor fusion algorithms that integrate data from multiple modalities, such as GNSS, LiDAR, radar, and cameras, to estimate real-time state estimation and tracking.
• Develop algorithms for outlier rejection, fault detection, and state smoothing.
• Contribute to architecture development, concept-of-operations, and technology trade studies.
• Collaborate with AI/ML engineers to explore machine learning-enhanced state estimation techniques