Machine Learning Specialist

Feladatok

Develop and implement machine learning models for space-related applications, such as satellite imagery analysis, anomaly detection in spacecraft telemetry and predictive maintenance.
Collaborate with engineers, mission planners and software developers to integrate AI solutions into flight (and possibly ground) systems.
Analyse large-scale, complex datasets from satellite sensors and onboard instruments.
Optimize algorithms for performance, reliability and deployment in resource-constrained environments (e.g., edge computing on spacecraft).
Monitor developments in AI and space research to incorporate the latest findings and innovations into our work.
Ensure all solutions meet the safety, reliability, and cybersecurity standards required in aerospace systems.

BSc, MSc or PhD degree in Computer Science, Aerospace Engineering, Physics, or a related technical field.
Solid experience in machine learning and data analysis, with a strong grasp of both traditional and deep learning methods.
Proficiency in Python and ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch)
Ability to think at system-level and work across multiple disciplines.
Ability to work independently.
Good problem-solving skills.
Proficiency in English

Experience with any embedded RTOS or Linux.
Experience in embedded SW development and C/C++ programming languages.
Experience working with satellite data, sensor fusion, or remote sensing is highly desirable.
Familiarity with ISO and/or ECSS standards and aerospace simulation tools (e.g., STK, ESA/NASA data formats).
Programming experience in scripting and automation.
Any experience in FPGA and hardware development.

Location & onsite ratio: 50% onsite in the 11th district