Reinforcement Learning Engineer

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.

• Competitive compensation
• Relocation support
• Individual learning budget
• Modern aerospace campus with an on‑site canteen and gym
• Free beverages
• Lunch support
• Tax‑free employee benefits
• Flexible working hours
• Regular remote work
• 30 days of vacation

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

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.

• 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.

• Prior experience to collaborative Multi Agent Systems

At Neural Agent, we are building coordination and AI-Operating layer at edge that orchestrates the autonomous systems and networks across space, air, ground, maritime and underwater domains.

We believe the future will be powered by decentralized intelligence—where autonomous systems collaborate, make decisions and adapt in real time wherever the mission takes place.