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Reinforcement Learning Engineer

Gauting
Full-time
Permanent employee

Why us?

You'll join a company where your work has visible impact from day one.
You'll work directly with world-class engineers, scientists, aerospace experts and entrepreneurs building technologies that have never existed before.
Every day you'll collaborate with people who genuinely believe Europe can lead the next generation of autonomous intelligence.
We're not maintaining legacy systems.
We're creating the infrastructure future generations will depend on.


What We Offer
Competitive Compensation
Personal Development
Lunch Support
Modern Aerospace Campus

Join Us
If you're ready to build technology that could shape Europe for decades to come, we'd love to meet you

Your mission

You will  design and train RL models in Multi Agent System context. Your work will be deployed to constrained devices, working collaboratively in the sky and space.


Your profile

  • Master's degree or PhD in Computer Science, Engineering or a related field.
  • Initial work experience.
  • Proficiency in Python and first experience in Rust
  • Proven experience adapting and deploying RL policies on edge or resource-constrained hardware across diverse customer environments; you will be working with a defined system, not building from scratch
  • Deep experience integrating learned policies with real hardware constraints including sensors, actuators, and latency requirements
  • Comfortable with fully on-device inference and decision-making without cloud dependency
  • Excellent communication skills in English; German language skills are a plus.
      Nice to Have
  • Prior experience to collaborative Multi Agent Systems

Über uns


Bei NeuralAgent entwickeln wir eine Koordinations- und KI-Betriebsschicht am Edge, die autonome Systeme und Netzwerke über die Domänen Weltraum, Luft, Boden, See und Unterwasser hinweg orchestriert.

Wir glauben, dass die Zukunft von dezentraler Intelligenz angetrieben wird – in der autonome Systeme zusammenarbeiten, Entscheidungen treffen und sich in Echtzeit anpassen, ganz gleich, wo die Mission stattfindet