Introduction
IBM Research Scientists are charting the future of Artificial Intelligence, creating breakthroughs in quantum computing, discovering how blockchain will reshape the enterprise, and much more. Join a team that is dedicated to applying science to some of today’s most complex challenges, whether it’s discovering a new way for doctors to help patients, teaming with environmentalists to clean up our waterways or enabling retailers to personalize customer service.

Your Role and Responsibilities
This is for a 2025 summer internship with the following start dates: May – August or June – September for quarter system schools.

Artificial intelligence is having a profound impact on all aspects of our lives and is transforming how work is conducted in every industry. Today, AI systems are enabling businesses to personalize services, converse with customers, automate operations, optimize workflows, predict demand, and recommend next best actions. A common thread to our ambitious AI and watsonx Research agenda is to understand how AI systems and algorithms can be designed responsibly and produce effective outcomes for their enterprise users.

We are seeking intern candidates to help us advance our research and development agenda on artificial intelligence and foundation models in areas including Natural Language Processing, CodeLLMs, Agents and Agentic Workflows, Generative AI Applications for Code, Time-Series, Business and IT Automation, and API Composition and Orchestration.

You have a proven interest and experience in defining and driving a research agenda for the duration of the internship with the goal to publish your work at top academic venues. During your internship you will work in close
collaboration with other researchers and engineers to conduct world-class research and software development. Demonstrated communication skills are essential.

Required Technical and Professional Expertise

• Applicants should be PhD & MS students
• Programming languages: Python, Java, C/C++, JavaScript, R, etc.
• Software engineering best practices, including agile techniques
• Cloud-native development and toolkits such as Docker, Kubernetes, and OpenShift
• Machine learning engineering: creating training pipelines and evaluating models using toolkits such as PyTorch, TensorFlow, and scikit-learn
• Design, validation, and characterization of algorithms and/or systems
• Machine learning theory: discriminative models, generative models, deep neural networks, detecting and mitigating bias, adversarial robustness, causality, uncertainty
• Backend storage technologies such as SQL and NoSQL databases such as Postgres, MongoDB, Cloudant, ElasticSearch, etc.

Preferred Technical and Professional Expertise

• Experience analyzing large-scale data from a variety of sources
• Experience publishing scientific results in technical communities such as NeurIPS, ICML, ICLR, IJCAI, ACL, AAAI, KDD, CHI, IUI, CSCW, or similar
• Experience in training and evaluating large-scale machine learning models, especially LLMs.
• Experience with program analysis tools and practices
• Qualitative and quantitative user research and user-centric design
• Experience solving analytical problems using rigorous and quantitative approaches
• Experience in front and back-end web application development and frameworks such as HTML, CSS, Bootstrap, Carbon, React, Flask, Node.js, etc.