Senior Robotics Engineer

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 Senior 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. #J-18808-Ljbffr THRYVE