Ai - Ml Engineer H/F - Infinite Orbits

About the Company

Established in 2017, Infinite Orbits is a NewSpace company, based in Toulouse, pushing the boundaries of in-orbit services with cutting-edge spacecraft driven by its own Rendez-Vous solution, an advanced Autonomous Vision-Based Navigation Solution. IO has two generations of missions

"Orbit Guard" provides Inspection and Space Situational Awareness services in GEO. Our first, OG-1 was launched on 1 May 2023, the second is launching in 2025 and the third third launching in 2026.

"Endurance" is a docking service capable of extending the life of GEO telecom satellites, by taking over AOCS takeover for 5 years.


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.

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.