Key Responsibilities

• Design, implement, and evaluate adaptive embodied AI agents that operate under uncertainty and adjust behavior at test time based on context, feedback, or human interaction.
• Design and implement methods that use deployment evidence to revise an agent's task representation, state or world model, policy, memory, or another appropriate component in its decision-making apparatus.
• Develop and experiment with multimodal models and agents, integrating vision, language, and action for embodied or interactive settings.
• Build or extend experimental tasks in embodied AI, robot learning, or related interactive simulation environments.
• Compare relevant adaptation mechanisms, such as in-context updates, retrieval, explicit memory revision, parameter-efficient optimization, fast weights, or world- and action-model adaptation. 
• Run controlled experiments, analyze results, and iterate on model and system design based on empirical findings.
• Document methodologies and findings through technical reports, presentations, and research papers.
• Collaborate with cross-disciplinary team members and participate in research discussions and design reviews.

Minimum Qualifications

• Currently enrolled as a Ph.D. or M.S. student in Computer Science, Machine Learning, or a related field at a reputable university.
• Research experience with projects related to embodied learning, imitation learning, reinforcement learning, meta-learning, transfer learning, or lifelong learning.
• Experience with open-source ML/AI frameworks (PyTorch, etc.).

Bonus Qualifications​