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

Loft Orbital; Toulouse; Presencial; EUR 13.000 - 16.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Lunch vouchers

Mobility package

Mentoring program

International environment

Descripción de la vacante

Loft Orbital in Toulouse invites a final-year engineering or Master’s student to join our AI for Space team as an ML internship focused on System 1 vs System 2 models. You will prototype fast, lightweight models and assess their value across our satellite operations, from detection to tasking.

The internship runs 6 months starting early 2027, offers €1,300 gross per month, lunch vouchers, mobility package and a mentoring program in a genuinely international setting with 20+ nationalities.

Formación

• Final-year engineering or Master’s student specialized in ML / deep learning / computer vision.
• Strong Python and PyTorch skills.
• Genuine interest in space and exposure to satellite data.

Responsabilidades

• Review the state of the art on System 1 / fast models and compare with System 2 models.
• Map where System 1 could add value in the operational chain from detection to tasking.
• Define benchmark scenarios and latency/accuracy budgets for each scenario.
• Train or fine-tune System 1 models and benchmark against System 2 baselines.

Conocimientos

Python

PyTorch

Deep learning

Computer vision

ML

Educación

Final-year engineering student

Master's student in ML/CV

Herramientas

PyTorch

Descripción del empleo

Wanna join the adventure?

Loft Orbital flies other people's payloads on standardized satellites, giving our customers a fast, simple, and reliable path to orbit. Our AI for Space team is looking for a motivated intern to explore how fast, lightweight “System 1” models can boost reactivity, performance, and usability across our satellite operational chains.

Reactivity use cases are at the heart of Loft's vision, and we are scaling them: from single-satellite reactive loops, to two-satellite / two-sensor tip & cue (a detection from one sensor automatically cueing an acquisition by another), and ultimately multi-satellite scenarios. Today we mostly rely on “System 2” models — powerful but slow and heavy at inference — from our partners. “System 1” models (fast, intuitive) are coming back strongly and could add value not only on reactivity, but also on performance and ease of use. This internship will assess that added value and prototype it on our reactivity scenarios.

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

• Gratification: €1,300 gross/month.
• Lunch vouchers (tickets restaurant), because let's be honest, we love food! (we even have a Slack channel about it: #loft-gourmand)
• Sustainable mobility package (forfait mobilité durable).
• A genuinely international environment comprising 20+ different nationalities.
• A genuine mentoring program to support you on a daily basis throughout the internship.