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.