Staff Robotics Engineer

Role : Staff Robotics Engineer (Robot Learning & Manipulation) 📍 Full-Time | Permanent

Location : Various location in Germany

Build the Future of Intelligent Robotics

THRYVE is partnering with a world-leading robotics company pushing the boundaries of robot learning, dexterous manipulation, and real-world autonomy.

We're looking for a Staff Robotics Engineer who thrives at the intersection of robotics, machine learning, and human-robot interaction. You'll help develop next-generation robotic systems that learn from human demonstrations and operate reliably in complex, unstructured environments.

This is an opportunity to work on cutting-edge manipulation challenges, leveraging teleoperation, imitation learning, and state-of-the-art AI to create robots capable of performing increasingly sophisticated real-world tasks.

What You'll Do

• Architect and develop robot learning pipelines for large-scale, high-quality data collection and training.
• Design and integrate advanced teleoperation systems, including force-feedback, haptics, and multimodal sensory interfaces.
• Advance robotic manipulation capabilities, with a particular focus on dexterous robotic hands and learning from human demonstrations.
• Collaborate closely with world-class AI researchers, roboticists, and software engineers to move cutting-edge research into deployed systems.
• Contribute to the full lifecycle of robotic learning systems, from data acquisition and model development through to real-world deployment and validation.

What We're Looking For

• Strong expertise in robot learning, teleoperation, imitation learning, and human-in-the-loop robotics.
• Understanding of Vision-Language Models (VLMs), Vision-Language-Action (VLA) architectures, and their application to embodied AI and robotic manipulation.
• Deep understanding of manipulation, control systems, and real-world robotic deployment challenges.
• Advanced programming skills in C++ and Python.
• Hands-on experience with ROS / ROS2 and modern robotics software stacks.
• Experience developing and deploying robotic systems beyond simulation.

Highly Desirable

• Diffusion-based policies and generative models for robotics.
• Learning from Demonstration (LfD) or Reinforcement Learning.
• VR/AR-based teleoperation interfaces.
• Unity, Isaac Sim, MuJoCo, or similar simulation environments.
• Experience working with dexterous hands, tactile sensing, or embodied AI systems.