Key Responsibilities

• Design, implement, and evaluate adaptive AI agents that operate under uncertainty and adjust behavior at test time based on context, feedback, or human interaction.
• Conduct research on human–AI collaboration, including interaction protocols, shared autonomy, and mechanisms for incorporating human guidance into agent decision-making.
• Work with simulation environments (e.g., VirtualHome, Habitat, or related embodied AI platforms) to build tasks, scenarios, and evaluation frameworks.
• Develop and experiment with multimodal models and agents, integrating vision, language, and action for embodied or interactive settings.
• Investigate reasoning and agentic behaviors, such as planning, decomposition, memory, tool use, or self-reflection in adaptive systems.
• 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 M.S. or a Ph.D. student in Computer Science, Machine Learning, or a related field at a reputed university.
• Research experience in projects related to reinforcement learning, meta learning, transfer learning, lifelong learning.
• Experience in open-source deep learning frameworks.

Bonus Qualifications