Algorithm Engineer

Responsibilities

1. Follow advances in Physical AI, read recent papers, reproduce representative algorithms and models on classical computing platforms such as GPUs, and establish training and evaluation baselines.
2. Follow advances in quantum computing and help researchers implement new algorithms and methods that integrate it. Implement complete solutions or individual modules from research ideas, including translating problems into quantum-solvable formulations such as Ising or QUBO.
3. Run and tune algorithms and models on quantum computing platforms. Design and conduct experiments comparing quantum and classical approaches, document experiments, and analyze results to evaluate their practical value.
4. Deploy reproduced or internally developed models on real robots, and carry out hardware debugging, operation, and performance validation.
5. Deploy and deliver algorithms for customer use cases. Adapt requirements, handle on-site deployment, and resolve issues to ensure reliable operation in real business environments.
6. Implement algorithms for data refinement, augmentation, and quality evaluation, continuously improving their results and performance.
7. Maintain experiments, regression tests, and technical documentation so that delivered results are reproducible and can be handed over.

Requirements

1. A master’s degree or higher in computer science, artificial intelligence, automation, robotics, or a related field. Exceptional bachelor’s graduates will also be considered.
2. Proficiency with AI agent tools such as Codex or Claude Code, or experience building and regularly using your own agents. Please demonstrate a project, experiment, or demo created in collaboration with AI during the interview.
3. Strong programming skills: proficiency in Python and C/C++, familiarity with deep learning frameworks such as PyTorch, and experience with GPU training and inference workflows.
4. Familiarity with embodied AI development tools, such as Isaac Sim / Isaac Lab, ROS/ROS2, and common tools for data collection, selection, and processing. The ability to combine multiple tools to complete research and development tasks.
5. Knowledge of mainstream Physical AI models and algorithms, including Diffusion Policies, VLA/VLM, reinforcement learning, and world models, and the ability to quickly understand and reproduce recent papers.
6. Hands-on experience deploying and debugging models on real robots.
7. Basic knowledge of quantum computing, or the ability to quickly learn its methods and tools and apply them in practice.
8. The ability to implement algorithm modules from research ideas and design, document, and validate comparative experiments.
9. Clear communication of your work and conclusions, effective collaboration with researchers and other colleagues, and the ability to solve practical problems in customer settings.

Preferred qualifications

• Publications in Physical AI, embodied AI, spatial reasoning, Gaussian Splatting, or related fields at leading conferences such as CoRL, RSS, ICRA, IROS, NeurIPS, ICLR, and CVPR, or journals such as T-RO, IJRR, and RA-L.
• The ability to independently turn research ideas into complete algorithm solutions.
• Deployment experience in bimanual manipulation, dexterous hand manipulation, or whole-body motion control.
• Experience using quantum computing platforms or implementing QUBO/Ising models or quantum-inspired algorithms.
• Open-source contributions, model deployment performance optimization, or experience delivering real projects.

How to apply