🇺🇸 2025 Intern: Research Scientist - Human-Centered Generative AI Internship

We are looking for interns with a background in HCI, Data Visualization, Interaction, Visual, and Experimental Design, or similar who can help us tackle these new challenges. Topics of interest include but are not limited to new techniques and approaches for visual data exploration & analysis, novel user interfaces and visualizations for effective large language model (LLM) prompting, trust & reliance in LLM outputs, explainability of LLMs, including source attribution & rationale generation, foundation model selection and comparison for enterprise use cases, foundation model risk assessment, governance & risk mitigation techniques, understanding synergy in human-AI collaborations, and human-centered and task-specific evaluation techniques and benchmarks.

- Conduct Research on Human-AI Interaction: Investigate user perspectives on making generative AI systems safe and trustworthy, addressing potential risks and harms, and designing effective human-centered AI solutions.
- Develop and Prototype User Interfaces and Visualizations: Create novel user experiences and visualizations for interacting with large language models (LLMs), including interfaces for effective prompting, source attribution, rationale generation, and trust-building.
- Collaborate on Foundation Model Assessment: Contribute to evaluating foundation models for enterprise use cases, including risk assessment, governance, model comparison, and task-specific benchmarks.
- Perform User-Centered Design and Evaluation: Conduct qualitative and quantitative user research to design and validate innovative interfaces, visualizations, or workflows that enhance human-AI collaboration.

- Applicants should be PhD & MS students pursuing graduate studies.
- HCI, Data Visualization, Interaction, User Research, Interaction Design, Visual Design, or Experimental Design.
- Basic knowledge in Large Language Models and Generative AI.
- Qualitative and quantitative user research and user–centric design.
- Programming skills in Python, Java, C++/C, etc.
Experience publishing scientific results in technical communities such as CHI, IUI, DIS, CSCW, IEEE Vis, EuroVis, NeurIPS, ICML, ICLR, IJCAI, ACL, AAAI, or similar.
- Machine learning techniques and machine learning toolkits such as PyTorch, Tensorflow, scikit–learn etc. including programming on GPUs.
- Experience in web application development and frameworks including HTML, CSS and JavaScript, JQuery, React, Flask, Node.js etc.
- Backend storage technologies such as SQL and Non–SQL databases (Postgres, MongoDB, Cloudant, ElasticSearch etc.)
- Experience in Kubernetes CI/CD tools and operators, IBM Cloud and machine learning training pipelines.
- Software engineering practices including agile techniques.
- System building/debugging/testing skills.
- Solving analytical problems using rigorous and quantitative approaches.

TBD