Senior Robotic Foundation Model Engineer (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.

Execute the full pipeline: Take foundation models to production-ready robotic intelligence by executing the complete NeuraGym pipeline — teleoperation, data annotation, training, simulation, deployment, and validation — tailored to real-world robotics applications. Define the data strategy that makes a fine-tune work and own the full post-training loop including hyperparameter iteration, data mixture, LoRA, and honest evaluation.

Deploy to real-time control: 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.

Master the full robotics stack: Work across NEURA OS and the control layer — real-time control, kinematics, motion planning, force/impedance control, and the safety architecture — and integrate learned policies cleanly with the low-level controller instead of treating it as a black box.

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

Enable and support customers: 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 Cloud and AI teams, ensuring smooth collaboration, technical alignment, and efficient problem-solving across domains.

Build evaluation rigor: Create the eval harnesses, success-rate benchmarks, and regression tests that decide whether a policy is ready for handover — and make failure modes visible before the customer finds them.

Research to production: Prototype techniques from recent papers together with the research team and bring the ones that survive contact with reality into production deployments.

Close the 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

Your education: Master's degree or PhD in Computer Science, Robotics, Electrical Engineering, or a related field.

ML in robotics depth: 5+ years of 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.

Fine-tuning expertise: 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.

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

Cloud and simulation: AWS, Azure, or GCP experience and familiarity with robotic simulation tools (IsaacSim, MuJoCo, etc.) are pluses.

Robotics software stack: Working command of a robotics software stack beyond the model layer — robot operating systems and middleware (NEURA OS, ROS/ROS2 or comparable), real-time control, kinematics and motion planning, force/impedance control, and how a learned policy wires into a low-level controller and its safety layer.

Hardware literacy: 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 distinguish a model problem from a machine problem on site.

Execution mindset: Proven problem-solving abilities, 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.

Communication: Clear communicator who translates between researchers, engineers, and end-users. You have solid command of English and German (B2–C1 level).

Travel readiness: Willingness to travel frequently to customer sites and NeuraGyms (approximately 40–60%).