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NEURA Robotics is seeking an experienced ML in robotics specialist to drive cognitive robotics applications on customer robots using the NeuraGym platform. You will own data strategy, training, simulation, deployment, and evaluation across the full pipeline.
You will integrate learned policies with real-time control, work across NEURA OS and the robotics stack, and support customers with hands-on guidance and troubleshooting. Fluency in English and German is expected.
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.
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%).