AI/ML Engineer

Darwin Space are currently hiring for an AI/ML Engineer on behalf of a business in Toulouse.

About the Role

They are an AI/ML Engineer to build and deploy learning-driven capabilities within their 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.

Sebastian Prins