AI Researcher, Robotics & Agentic AI

We are seeking a skilled AI Researcher to advance foundation models for robotics, Physical AI, and agentic AI, with a focus on VLA, VLM, and World Models. This role will work on training, fine-tuning, evaluating, and optimizing large AI models for real-world robotic and edge AI applications.

The successful candidate will collaborate closely with university labs, product teams, and hardware/software platform teams to explore next-generation AI model capabilities and translate research outcomes into robust production-ready solutions.

Key Responsibilities:

• Conduct research and engineering work on model training, fine-tuning, post-training, distillation, alignment, and simulation-based optimization for robotics and agentic AI scenarios.
• Research and prototype agentic AI systems that combine foundation models with planning, tool use, memory, retrieval, environment interaction, and task execution.
• Improve model capabilities for agentic workflows, including function calling, tool selection, multi-step reasoning, self-reflection, task decomposition, long-horizon planning, and failure recovery.
• Collaborate closely with university labs and research partners to explore emerging model architectures, evaluate research prototypes, and translate promising technologies into deployable solutions.

Required Qualifications:

• BS/MS degree in Computer Science, Software Engineering, Electrical Engineering, Robotics, Artificial Intelligence, or equivalent practical experience.
• Solid understanding of the architecture, training, fine-tuning, and deployment methodologies of mainstream large AI models, including LLMs, VLMs, VLA models, or World Models, with an ability to actively track research progress and assess practical value.
• Familiarity with agentic AI concepts such as tool use, function calling, planning, memory, RAG, autonomous task execution, and multi-step reasoning.
• Understanding of agent system design patterns, including planner-executor, ReAct-style reasoning, retrieval-augmented agents, tool-using agents, and multi-agent collaboration.
• Excellent communication skills in English, with the ability to collaborate effectively across research, engineering, product, and external academic teams.
• Strong programming skills in Python and Shell; familiarity with C/C++ for model integration, deployment, or performance optimization.

Preferred Qualifications:

• Experience with supervised fine-tuning, LoRA/QLoRA, knowledge distillation, preference optimization, continual learning, imitation learning, reinforcement learning, and simulation-to-real transfer.
• Hands-on experience with open-source robotics or Physical AI models such as OpenVLA, RT-1/RT-2, RDT, diffusion policy models, pi0-style models, or related architectures.
• Experience in building tool-using agents, local assistants, robotics agents, workflow agents, code agents, or domain-specific autonomous systems.
• Experience designing agent evaluation methods, including task success rate, tool-call accuracy, planning quality, grounding quality, safety, robustness, and latency.