Introduction
At IBM, work is more than a job - it's a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you've never thought possible. Are you ready to lead in this new era of technology and solve some of the world's most challenging problems? If so, lets talk.
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.
IBM Research is looking for strong BS, MS and PhD level interns to join our team in 2025 to work in the area of large scale langage models. Our team directly contributes to the construction of IBM's largest scale AI models as well as the software supporting them, and we have a range of possible internship projects in the general areas of model architecture, analysis, training and alignment techniques, reasoning and planning, tool use, multilinguality, curation of challenging benchmarks and human preference data, structured input and output and programming models for LLM applications.
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In this role, you are expected to develop high quality software to support novel AI model architectures, training and evaluation technologies, new synthetic data generation and transformation techniques, tools for interacting and collecting data from experts, and LLM application development frameworks, among possible projects. Contributions to the open source are a potential desirable outcome of projects in this space.
Required Technical and Professional Expertise
- Applicants should be BS students.
- Strong familarity with one or more of: python, pytorch, huggingface, langchain, langgraph, autogen, c/c++, javascript and client/server frameworks, as necessary to support an internship project.
- Experience on design, validation, and characterization of algorithms and/or systems in at least one of the areas relevant above.
- Familiarity with open source technologies in general (e.g. github).
Preferred Technical and Professional Expertise
- Basic coursework on machine and deep learning.
- Experience in training large-scale machine learning models.
- Experience analyzing large-scale data from a variety of sources.