Research AI Engineer Intern
@ IBM

San Jose, CA
$30,000
On Site
Intern
Posted 21 days ago

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Job Details

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
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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.

Frequently Asked Questions