Algorithm Researcher

Responsibilities

1. Analyze the limitations and opportunities for improvement in generative models for Physical AI, including Diffusion Models / Diffusion Policies, encoder-decoder architectures, VLA/VLM, reinforcement learning, and world models. Propose new model architectures that integrate quantum computing.
2. Translate problems into standard models or mathematical formulations that quantum computers can solve, such as Ising or QUBO. Conduct theoretical analysis, develop prototypes, and use quantum computers to solve and validate them.
3. Research algorithms for data refinement, augmentation, and quality evaluation, and develop reusable methods and modules.
4. Establish classical algorithm baselines and evaluation methods. Validate the benefits and applicability limits of quantum approaches through reproducible controlled experiments.
5. Produce both research and practical outcomes: publishable papers and technical reports, as well as reusable algorithm capabilities that support delivery to customers.
6. Collaborate with engineering and other colleagues to turn research results into engineering capabilities.

Requirements

1. A background in computer science, artificial intelligence, mathematics, physics, robotics, or a related field. A PhD is preferred, including recent graduates and postdoctoral researchers; exceptional master’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 mathematical modeling skills, with the ability to abstract real-world problems into standard mathematical formulations suitable for quantum computing.
4. Substantial research experience in generative models. Familiarity with at least one of Diffusion Models / Diffusion Policies, encoder-decoder architectures, VLA/VLM architectures, reinforcement learning, or world models; an understanding of their principles, limitations, and possible improvements; and hands-on experience training models and designing or improving model architectures.
5. Research experience in robotics, including validating models on real robots or conducting research in high-fidelity simulation environments and publishing related papers. Experience in bimanual manipulation, dexterous hand manipulation, or whole-body motion control is preferred.
6. The ability to independently read papers, formulate questions, design experiments, and implement code prototypes, with attention to experiment records and reproducibility.
7. A willingness to deliver both research and practical results, and to continue learning quantum computing through interdisciplinary collaboration.

Preferred qualifications

• Publications at leading conferences or journals in relevant fields, such as NeurIPS, ICML, ICLR, CoRL, RSS, ICRA, CVPR, ICCV, SIGGRAPH, T-RO, IJRR, or TPAMI; open-source research projects; or demonstrable algorithm innovations.
• Research experience in quantum computing, QUBO/Ising modeling, coherent Ising machines (CIM), or quantum-inspired methods.
• Research experience in energy-based models, sampling optimization, or related topics.

How to apply