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# Technical Lead - AI Manipulation and Simulation (human)

- **Company:** Neura Robotics GmbH
- **Location:** Metzingen, Germany
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Learning Management Systems, Python (Programming Language), Reinforcement Learning, Real Time Systems, Information Technology, Data Pipelines
- **Published:** June 18, 2026
- **Apply:** https://de.indeed.com/viewjob?jk=d45f4bfb3f9005aa

## About the Role

Do you have experience in Research?, * 6 or more years of experience in computer science or a related engineering field, with meaningful time delivering AI systems on physical robotic hardware

 * Hands-on experience leading or co-leading the design of multimodal manipulation systems combining vision, language, tactile, and proprioceptive inputs
 * Proven track record building simulation infrastructure (Isaac Lab and Isaac Sim, or MuJoCo) for reinforcement learning and sim-to-real transfer
 * Deep practical knowledge of imitation learning (including diffusion policies), deep RL, and hybrid learning approaches on real robot hardware
 * Experience with data pipelines for heterogeneous, high-frequency sensor data: teleoperation, tactile, vision, depth, and robot state
 * Strong Python and C++, with experience in ROS2 and embedded or real-time systems
 * Ability to translate ambiguous research problems into concrete engineering milestones and to grow junior engineers

Nice to have:

 * Experience with high-DOF, tendon-driven, or tactile-heavy dexterous hands
 * Familiarity with VLA or vision-language-action architectures and large-scale pre-training workflows
 * Contributions to the robotics or AI research community (ICRA, IROS, CoRL, NeurIPS)

## Description

* Multimodal Learning Architecture: You design and build systems that fuse camera, depth, tactile, IMU, language, and robot-state inputs into executable robot actions. You define how these modalities combine and translate into policies that run on our dexterous hands.
 * Simulation Infrastructure & Data Pipelines: You build and maintain GPU-accelerated simulation environments and the data pipelines that make policy training scalable.
 * Sim-to-Real Transfer: You lead the engineering work that closes the gap between simulation and physical hardware.
 * Team Leadership & Engineering Direction: You translate research problems into concrete milestones, guide engineers, and work across ML, robotics, and hardware teams to ship learning systems that run on physical robots.


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