AI/ML Engineer

Infinite Orbits; Toulouse; On-site; EUR 65,000 - 98,000; Full time

Job summary

Infinite Orbits in Toulouse is seeking an AI/ML Engineer to build and deploy learning-driven capabilities within autonomous space systems. You will create models that operate in closed-loop autonomy, supporting perception, navigation, and decision-making in real mission environments.

This hands-on role focuses on real spacecraft deployment rather than offline evaluation. Responsibilities include designing and validating ML models for rendezvous, formation flying, on-orbit inspection, and space

Qualifications

• Strong foundation in machine learning, deep learning, reinforcement learning, or a related technical discipline.
• 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.

Responsibilities

• 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.

Skills

ML knowledge

Python

C++

PyTorch or TensorFlow

Model deployment

Tools

Simulation tools

Data generation pipelines

Job description

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.

About the Role

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.

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.