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Research AI Engineer Intern

IBM

San Jose, CAOn Site

Original Job Summary

Introduction

IBM Research takes responsibility for technology and its impact on society. Joining IBM Research means inventing what’s next in computing and working on mind-bending, impactful projects.

Your Role And Responsibilities

As a Research AI Engineer Intern, you will:

  • Collaborate with AI researchers and engineers.
  • Design, prototype, and implement AI solutions.
  • Work with large language models, multimodal models, and agentic frameworks.
  • Develop AI agent frameworks for scalable orchestration and tool integration.
  • Create experimental demos and proof-of-concepts.
  • Stay current with recent AI research and apply new methods.

Preferred Education

Bachelor's Degree

Required Technical And Professional Expertise

  • Pursuing a Master’s degree in Computer Science, Engineering, or related field.
  • Strong programming skills in Python.
  • Familiarity with machine learning fundamentals, natural language processing and generative AI.
  • Excellent problem-solving ability and teamwork skills.

Preferred Technical And Professional Experience

  • Pursuing a PhD.
  • Understanding of MLOps concepts including model deployment, monitoring, and scaling.
  • Experience with open-source contributions, research projects, or hackathons related to AI.

Key skills/competency

  • AI
  • Machine Learning
  • Python
  • NLP
  • Generative AI
  • MLOps
  • LLMs
  • Research
  • Prototyping
  • Problem-solving

How to Get Hired at IBM

🎯 Tips for Getting Hired

  • Research IBM's culture: Study IBM mission, values, news, and employee reviews.
  • Customize your resume: Highlight relevant AI and Python skills.
  • Build a strong portfolio: Include research projects and demos.
  • Practice technical interviews: Prepare on AI, coding, and problem solving.

📝 Interview Preparation Advice

Technical Preparation

Review Python and AI libraries.
Practice coding algorithms in Python.
Study LLMs and NLP techniques.
Review ML deployment and MLOps practices.

Behavioral Questions

Describe a challenging team project.
Explain a time you solved complex problems.
How do you handle tight deadlines?
Share an example of adapting to change.