Research Engineer | Reinforcement Learning & Robotic Manipulation

AI Futures; München; On-site; EUR 90,000 - 140,000; Full time

Benefits offered by this job

Access to real hardware

Ownership from research to production

Collaborative engineering environment

Competitive salary

Job summary

Ai Futures is seeking a research-focused role in Munich to advance reinforcement learning for precise, contact-rich robotic manipulation. You will build large-scale simulation training environments and drive transfer of learned behaviours from simulation to real hardware.

Join a collaborative engineering team to blend learning-based methods with classical control for stability, take approaches into production, and contribute to real hardware testing with ownership from research to production.

Qualifications

• Hands-on experience applying RL to robotic control.
• Solid grounding in robotics, control theory and dynamics.
• Experience building and scaling simulation and training pipelines.
• Track record of impact: deployed systems, publications or open-source work.

Responsibilities

• Develop reinforcement learning approaches for precise, contact-rich manipulation.
• Build large-scale training and evaluation environments in simulation.
• Transfer learned behaviours from simulation to physical hardware.
• Blend learning-based methods with classical control for stability and accuracy.
• Work with the wider engineering team to take successful approaches into production.

Skills

Reinforcement Learning

Robotics

Control theory

Simulation pipelines

Research impact

Job description

We’re partnering with a growth-stage robotics company with commercially deployed systems. The team is building out its learning-based manipulation capability and is hiring for a key research position.

The Role

• Develop reinforcement learning approaches for precise, contact-rich manipulation.
• Build large-scale training and evaluation environments in simulation.
• Transfer learned behaviours from simulation to physical hardware.
• Blend learning-based methods with classical control for stability and accuracy.
• Work with the wider engineering team to take successful approaches into production.

What We’re Looking For

• Hands-on experience applying RL to robotic control, from simulation to real hardware.
• Solid grounding in robotics, control theory and dynamics.
• Experience building and scaling simulation and training pipelines.
• A track record of impact: deployed systems, publications or open-source work.

What’s On Offer

• Access to real hardware for testing.
• Ownership of your work from research through to production.
• A collaborative engineering environment where your work shapes the product.
• Competitive salary and meaningful technical challenges.