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.
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.
Who We Are
Loft: Space Made Simple.
Founded in 2017, Loft provides governments, companies, and research institutions with a fast, reliable, and simple way to deploy missions in orbit.
We integrate, launch, and operate spacecraft, offering end-to-end missions as a service across Earth observation, IoT connectivity, on-orbit AI, national security missions, and more. Leveraging our existing space infrastructure and an extensive inventory of satellite buses, Loft is reducing years-long integration and launch timelines to months. With more than 30 missions flown, Loft’s flight heritage and proven technologies enable customers to focus on their mission objectives.
With a growing fleet on track to reach 30 satellites by 2027, we are scaling up quickly across our offices in San Francisco, CA | Golden, CO | and Toulouse, France to meet accelerating demand for space infrastructure.
As an international company your resume will be reviewed by people across our offices so please attach a copy in English.
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?
You will join the AI for Space team and directly support the scaling of reactivity use cases, a core part of Loft's vision — working toward shorter detection-to-tasking latency on single-satellite and tip & cue scenarios, and a first step towards multi-satellite reactive operations. You'll build new expertise on an emerging class of models, reusable across other detection-like use cases, with a concrete prototype and benchmark the team can build on. With a growing fleet on track to reach 30 satellites by 2027, we are scaling up quickly across our offices in San Francisco, USA | Golden, USA | and Toulouse, France.Some of our awesome internship benefits:
- 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.