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Robot Learning

Train the policies that generalize across tasks and embodiments — imitation learning, RL, and the messy middle where sim-to-real actually happens.

Location
Landshut, Germany
Employment
Full-time
Work model
On-site

At Athenyx Robotics, we are building the intelligence stack for industrial humanoid robotics. We integrate robot learning, perception and simulation with direct access to real industrial use cases and deployment environments — because generalization only matters if it transfers to a shift on Tuesday morning.

This role owns the learning side of that stack: from pretraining to policy deployment.

The work

  • —Develop and train learning-based control policies using imitation learning, reinforcement learning and behavior modeling approaches.
  • —Design end-to-end and modular policy architectures for manipulation, handling and inspection tasks with emphasis on generalization.
  • —Contribute to behavior and skill modeling, including abstraction, composition and policy conditioning.
  • —Train and evaluate models in simulation environments and facilitate simulation-to-real-world transfer.
  • —Collaborate with perception and robotics engineers to build integrated systems.
  • —Support integration, testing and deployment on physical robotic systems in industrial environments.

About you

  • —Bachelor's, Master's or PhD degree in robotics, computer science, machine learning or related field with strong robot-learning foundation.
  • —Strong Python programming skills and experience with modern ML and robotics workflows.
  • —Experience in reinforcement learning, imitation learning, policy learning, or emerging approaches like generalist policies or world modeling.
  • —Interest in real-world robotic systems and commitment to full-stack implementation.
  • —Willingness to work across multiple domains in a small team environment.
  • —Experience with simulation environments and sim-to-real transfer.
  • —Familiarity with perception pipelines (3D vision, pose estimation).
  • —Experience with manipulation or contact-rich interaction.
  • —Work on real robotic systems with direct industrial impact.
  • —Collaboration with industry partners and access to real-world use cases and data.
  • —High ownership and responsibility in a small, execution-focused team.
  • —Flexible working hours and open, collaborative culture.
  • —Opportunity to shape AI-driven robotics systems from inception.

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