Robotics Physical AI Expert

About the Role This role develops and optimizes embodied AI and robotics systems using fine-tuning, reinforcement learning, and imitation learning. It builds end-to-end training, evaluation, and deployment pipelines for complex robotic tasks. The position integrates perception, planning, and control components to improve robot performance. What You'll Do

• Develop and optimize embodied AI and robotics systems using fine-tuning, reinforcement learning, and imitation learning.
• Build end-to-end training–evaluation–deployment pipelines for complex robotic tasks.
• Integrate perception, planning, and control components; collaborate with software, simulation, data, and hardware teams for real-world deployment.
• Explore/adapt state-of-the-art robotics algorithms for practical business applications.
• Conduct secondary development and post-training on embodied AI pre-trained models: multimodal fine-tuning (Supervised Fine-Tuning/preference optimization/Reinforcement Learning), LoRA/Adapters, prompting & tool invocation, and action/control interface alignment, to improve task success rate, stability, and regression reliability on target use cases.
• Build a training–evaluation–iteration closed loop for complex/long-horizon tasks: dataset construction and versioning, offline replay, simulation validation, on-robot acceptance test scripts and metrics, enabling rapid issue localization and iteration.
• Apply reinforcement learning/imitation learning to manipulation tasks: assembly tolerances, grasp robustness, path/action optimization, recovery policy learning; build/select training frameworks (Offline RL/Online Fine-tuning/Residual RL, etc.) and define sim-to-real strategies and acceptance criteria.
• Participate in end-to-end embodied “brain” capability design: integrating environment perception model calls with VLA and closed-loop policy/planning with feedback; align interfaces and online constraints with the systems team.
• Collaborate with data/systems/simulation/hardware teams to complete real-robot integration and debugging, problem scoping, and performance optimization, ensuring stable operation under noise and communication delays.
• Track the frontier of embodied AI/robotics algorithms (e.g., ACT, Diffusion Policy, world models, etc.), conduct prototyping and engineering adaptation for business scenarios, and distill reusable algorithm components. What We're Looking For
• Master’s degree or above in Computer Science/Computer Vision/Automation/Robotics/Control/Artificial Intelligence, or related fields.
• 5+ years of R&D experience in embodied AI/machine learning/multimodal/reinforcement learning, with project experience delivering end-to-end deployment on real robot platforms for manipulation tasks (training/evaluation/deployment/integration).
• Familiar with robotics perception and manipulation fundamentals: pose estimation, calibration, point cloud/depth processing; understand control interfaces and engineering constraints (real-time/safety/tolerances).
• Strong algorithm foundations: multimodal learning, behavior/policy learning, reinforcement learning, model training and evaluation methods; candidates with Online RL/Offline RL training practice are preferred.
• Able to productionize algorithms: data flywheel/closed-loop, observability/regression, inference performance optimization, and integration with system/control pipelines.
• Proficient in Python with C++ engineering development capability; familiar with PyTorch (or TensorFlow); familiar with ROS/ROS2; candidates with simulation experience (Isaac Gym / Isaac Sim / MuJoCo) or sim-to-real real-robot deployment experience are preferred.
• Preferred: representative achievements such as top-tier conference/journal publications, open-source projects, patents, or pilot cases. Nice to Have
• Representative achievements such as top-tier conference/journal publications, open-source projects, patents, or pilot cases. Compensation & Benefits
• Competitive salary and performance-based incentives. Comprehensive health and wellness plans. Professional development opportunities and continuous learning programs. Flexible work arrangements to support work-life balance. Employee recognition programs and awards. Access to brand new technology and resources to support your work.