Les missions du poste
We're seeking an AI/ML Engineer to build and deploy learning-driven capabilities within our autonomous space systems. In this role, you'll create models that directly operate in closed-loop autonomy, supporting perception, navigation, and decision-making in real mission environments. This is a hands-on position for engineers who want their models running on real spacecraft, not just evaluated in offline experiments.
Job Description
- Design, train, and validate AI/ML models supporting satellite autonomy, including rendezvous and proximity operations, on-orbit inspection, formation flying, and space domain awareness.
- Develop learning-based components for guidance, navigation, perception, and decision-making, such as pose estimation, anomaly detection, behavior recognition, and trajectory generation.
- Build LLM- and VLM-powered tools to enable intuitive satellite operations, including natural language command interfaces and visual scene understanding.
- Create and maintain synthetic data generation pipelines that model realistic orbital dynamics, sensor behavior, lighting conditions, and uncertainty, using domain randomization and sim-to-real techniques.
- Extend and improve simulation environments used for training, verification, stress testing, and adversarial evaluation of autonomy models.
- Optimize models for flight deployment, applying techniques such as quantization, pruning, batching, and real-time inference on limited onboard compute.
- Integrate ML components into flight software using well-structured Python and C++ interfaces, with strong safeguards and fallback behaviors.
- Participate in software-in-the-loop and hardware-in-the-loop testing, closing the feedback loop between simulation, sensors, control systems, and model improvements.
- Take full ownership of your models, from early experimentation and training pipelines to testing, review, deployment, and long-term maintenance.
Le profil recherché
Requirements
- Strong foundation in machine learning, deep learning, reinforcement learning, or a related technical discipline.
- Practical experience developing, training, and evaluating ML models for perception, estimation, planning, or control systems.
- Proficiency with modern ML frameworks such as PyTorch or TensorFlow, and the ability to integrate models into Python and C++ systems.
- Experience building simulation tools or data generation pipelines to support ML development.
- Ability to design, debug, and scale training workflows, including dataset management, distributed training, and evaluation.
- Familiarity with deploying ML inference as part of larger autonomous or robotic software stacks.
- A sense of ownership, adaptability in fast-paced environments, and clear communication around technical tradeoffs.
Nice to Have
- Experience working with foundation models or large-scale pretrained architectures.
- Knowledge of orbital mechanics, spacecraft dynamics, or space systems.
- Experience combining learned models with classical estimation, guidance, or control algorithms.
- Background in sim-to-real transfer, domain adaptation, or operational ML systems.
- Exposure to embedded inference, GPU optimization, or real-time autonomous platforms.
- Publications, open-source contributions, or research experience in ML, robotics, autonomy, or reinforcement learning.
Infos complémentaires
What we offer
- The opportunity to be part of an international team transforming the space industry.
- A creative and innovative work environment where ideas turn into reality.
- Competitive salary and benefits.
Les étapes de recrutement
Les étapes de recrutement peuvent varier selon l'offre à laquelle vous postulez.
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Meeting with a talent recruiter.
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Meeting with operational team leaders / directors.
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Meeting with the HR Director.
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Infinite Orbits en images
Publiée le 23/03/2026 - Réf : 3901367/28062329 AE/31T