Research Scientist: Human-AI Co-Adaptation and Continual ...

Key Responsibilities

• Conduct original research on continual, interactive, and lifelong learning for multimodal AI systems.
• Develop methods that enable AI systems to adapt, personalize, and improve through interaction with human users.
• Investigate mechanisms for human-AI interactive learning, including lightweight adaptation, memory and retrieval, uncertainty-aware learning, and learning from human feedback.
• Develop algorithms that remain robust under distribution shifts, changing user populations, evolving tasks, and changing multimodal inputs.
• Advance methods for user modeling, personalization, and human-AI co-adaptation through repeated interaction.
• Design and utilize simulated and real-world environments to study adaptation, personalization, and human-AI collaboration.
• Design rigorous evaluation methodologies and benchmarks to measure adaptation, robustness, and long-term human-AI interaction.
• Conduct empirical analyses of adaptation dynamics, including sample efficiency, calibration, stability, and catastrophic forgetting.
• Collaborate with interdisciplinary teams and contribute to publications, patents, prototypes, and research innovations.

Minimum Qualifications

• Ph.D. in Computer Science, Robotics, Electrical Engineering, Machine Learning, Artificial Intelligence, or a related field.
• Strong research record in machine learning, artificial intelligence, robotics, or related disciplines, demonstrated through publications, impactful projects, or equivalent research contributions.
• Demonstrated ability to formulate, lead, and execute independent research programs.
• Deep expertise in one or more of the following areas:
• Continual learning, online learning, test-time adaptation, personalization, or learning from human feedback.
• Multimodal AI systems, including representation learning, alignment, grounding, or reasoning across modalities.
• Human-AI interaction, human-aware AI, assistive AI, or interactive learning systems.
• Embodied AI, decision-making systems, or adaptive AI systems operating in real time.
• Strong communication, presentation, and collaboration skills.
• 1 - 3 years of relevant work experience.

Bonus Qualifications

• Experience with continual learning, online learning, test-time adaptation, memory-augmented systems, retrieval-augmented systems, or learning from human feedback.
• Experience with personalization, user modeling, adaptive assistants, or long-term human-AI interaction.
• Experience with uncertainty estimation, calibration, and robust adaptation in multimodal systems.
• Experience with multimodal reasoning, agentic systems, planning, or decision-making in collaborative human-AI environments.
• Experience with simulation and benchmarking platforms for embodied AI, human-AI interaction, or multi-agent systems.
• Publication record at leading venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP, AAAI, RSS, or CoRL.