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

Loft Orbital is hiring an AI/ML Engineering Intern: System 1 Models for Reactive Space Operations in Toulouse, France. Level rates it ; you can apply on Loft Orbital.

Level's read on this job

Builds AI ●●●● is how Level rates this machine learning engineer role: the job is building AI systems.
Tools and skills the posting cites include PyTorch and computer vision.
Level classifies it as an internship.
Level lists 84 other open roles at Loft Orbital.
The role was posted in the last few days.

AI in this role

PyTorch

computer-vision

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.

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?

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.

*Research shows that while men apply to jobs where they meet an average of 60% of the criteria, women and other underrepresented people tend to only apply when they meet 100% of the qualifications. At Loft, we value respectful debate and people who aren’t afraid to challenge assumptions. We strongly encourage you to apply, even if you don’t check all the boxes.
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.

How we rate this

AI/ML Engineering Intern: System 1 Models for Reactive Space Operations at Loft Orbital rates 88 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

AI/ML Engineering Intern: System 1 Models for Reactive Space Operations is an open job at Loft Orbital.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in Toulouse, Occitanie.
The job was posted on November 10, 2026.
Seniority: intern.
Applications are handled by Loft Orbital. Apply on Loft Orbital

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Computer visionPyTorch

Also common in machine learning engineer jobs at this level

Machine learning TensorFlow AWS Generative AI C++Spark

Pay

Typical machine learning engineer job: $198k

This posting doesn't list pay. Use the typical machine learning engineer figure when pay comes up. See machine learning engineer jobs

Questions you could be asked

Walk me through a computer vision problem you solved, from raw data to a deployed model.
Walk me through how you've used PyTorch in your day-to-day work.
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: Computer vision and PyTorch. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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