IBM Research takes responsibility for technology and its role in society. Working in IBM Research means you'll join a team who invent what's next in computing always choosing the big urgent and mind-bending work that endures and shapes generations. Our passion for discovery and excitement for defining the future of tech is what builds our strong culture around solving problems for clients and seeing the real world impact that you can make.
IBM's product and technology landscape includes Research Software and Infrastructure. Entering this domain positions you at the heart of IBM where growth and innovation thrive.
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 Distributed (Edge) AI Trusted AI Scalable Data Engineering AI for Business and IT Automation AI Applications AI Security AI Hardware Automated AI Conversational AI 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.
• Programming lanaguages: 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 large language models detecting and mitigating bias adversarial robustness causality uncertainty
• Backend storage technologies such as SQL and NoSQL databases such as Postgres MongoDB Cloudant ElasticSearch etc.
• 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 large-scale machine learning models
• 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.
• Knowledge in one or more of the following topics: finetuning algorithms (e.g. LoRA DPO etc.) reinforcement learning model architectures (e.g. transformers state-space models etc.) LLM-based agents agentic workflows multi-agent systems RAG agent frameworks (e.g. LangGraph CrewAI etc.) time series data