Physical AI Engineer - NeuraGym (Aachen) (human)

Your mission & challenges

• Own the outcome on the robot: Drive the development of cognitive robotics applications through Physical AI and the NeuraGym platform — you own the outcome on the customer’s robot, not just the model in the repo.

• Training & fine-tuning cutting-edge models: Take foundation models to production-ready robotic intelligence by executing the full NeuraGym pipeline — including teleoperation, data annotation, training, simulation, deployment, and validation — tailored to real-world robotics applications.

• Get models running well on the robot: Take policies from checkpoint to real-time closed-loop control — distillation, quantization, action chunking, latency budgets, and on-device/edge inference under the customer’s cycle time and reliability requirements.

• Be the face of NeuraGym for our customers: Be the primary technical contact for customers adopting Physical AI. Build trusted relationships, understand their applications, and accompany them from kickoff to handover.

• Hands-on robotics: Work directly on MAiRA, MiPA, LARA, and our humanoid platforms together with multidisciplinary engineering teams — end-effectors and tool changers, sensor selection and mounting, calibration, electrical and fieldbus interfaces, and the mechanical reality of the cell.

• Create customer champions: Enable key people at customer organizations to train their own colleagues, so customers become self-sufficient.

• Customer enablement & support: Guide and onboard users to NeuraGym by reviewing training pipelines and scripts, identifying data quality issues, troubleshooting model behavior, and providing hands-on support to resolve persistent blockers.

• Cross-functional collaboration: Serve as a key interface between the Cloud and AI teams, ensuring smooth collaboration, technical alignment, and efficient problem-solving across domains.

• Evaluation & reliability: Build the eval harnesses, success-rate benchmarks, and regression tests that decide whether a policy is ready to hand over — and make the failure modes visible before the customer finds them.

• Product & research feedback loop: Translate field experience and customer insights into actionable feedback for both the NeuraGym product roadmap and the NEURA core AI roadmap.

What we can look forward to

• Master’s degree or PhD in Computer Science, Robotics, Electrical Engineering, or a related field.

• Hands-on machine-learning experience in robotics — strong in at least one of: vision-based manipulation, reinforcement/imitation learning, or multimodal models — and keen to learn the rest.

• Demonstrable experience fine-tuning large pretrained models for robotics or another domain: PEFT/LoRA and full fine-tunes, dataset curation, distillation, and evaluation. Familiarity with the open VLA and policy-learning ecosystem (e.g., π0/π0.5, OpenVLA, LeRobot, diffusion policies) is a strong plus.

• Solid Python skills (C++ a plus); practical experience with PyTorch or TensorFlow.

• Proven experience in customer-facing technical roles (e.g., solution engineering, field AI engineering, technical consulting), including managing several customers in parallel.

• Solid understanding of robot hardware — drives and joint modules, torque sensing and encoders, end-effectors, sensor integration, calibration, electrical and fieldbus interfaces (e.g., EtherCAT), and functional safety concepts — enough to tell a model problem from a machine problem on site.

• Proven problem-solving abilities, the ability to handle multiple projects in parallel, and a strong bias toward execution and personal commitment. You take deployments personally, work through setbacks, and measure yourself by what runs at the customer.

• Clear communicator who can translate between researchers, engineers, and end-users. You have a good command of English and German (B2–C1 level).

• Willingness to travel frequently to customer sites and NeuraGyms (approx. 40–60%).

We ensure a transparent and efficient process and look forward to getting to know you during the application process.