AI/ML Engineering Intern: System 1 Models for Reactive Space Operations

Location: Toulouse office, city center (Jeanne d’Arc metro station)
Duration: 6-month, end-of-studies internship — target start date: early 2027.

About this role:

• Review the state of the art on System 1 / fast, intuitive models and compare them with the System 2 models used today, including our partners' models.
• Map where System 1 models could bring value in our operational chain for reactivity use cases, from detection to tasking.
• Define benchmark scenarios of increasing complexity with the team: a single-satellite reactive loop, then a two-satellite / two-sensor tip & cue loop, and, as a stretch goal, a multi-satellite scenario.
• Set the latency budget, accuracy needs, and execution context for each scenario.
• Build datasets and evaluation sets from Loft data, partner outputs, and open datasets.
• Train or fine-tune System 1 models and benchmark them against System 2 baselines on reactivity (latency), performance (accuracy, compute footprint), and ease of use (integration and operational effort).
• Prototype an end-to-end reactive loop, possibly as a hybrid design where System 1 triggers and System 2 validates, and assess how it extends to multi-satellite coordination.
• Document results and recommendations for production use, and deliver a final demo.

Must Haves:

• Final-year engineering or Master's student specialized in ML / deep learning / computer vision.
• Solid Python and PyTorch skills, and good software engineering practices.
• Strong understanding of the state of the art in LLM / VLM / Reinforcement Learning.
• Practical experience training neural networks (language models or computer vision) and/or LLM manipulation.

Nice to Haves:

• Model optimization (quantization, distillation, edge inference).
• Exposure to satellite or geospatial data.
• Multi-agent or distributed systems experience.
• Genuine interest in space.

What Can We Offer?