Introduction

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.

Your Role And Responsibilities

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.

Preferred Education

Master's Degree

Required Technical And Professional Expertise

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.

Preferred Technical And Professional Experience

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

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Requirements

No specific requirements listed.