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Intern AI Platform Developer

IBM

New York, United StatesOn Site

Original Job Summary

Introduction

IBM Research is at the forefront of technology and innovation. Joining as an Intern AI Platform Developer means working with a team that invents the future of computing and shapes generations with mind-bending work.

At IBM, you will be part of a rich ecosystem involving Research, Software, and Infrastructure, positioning you in a dynamic environment where growth and innovation thrive.

Your Role And Responsibilities

As an Intern AI Platform Developer, you will:

  • Design and develop software platforms for AI and machine learning.
  • Create frameworks, tools, and infrastructure for the AI lifecycle.
  • Work on components such as data ingestion, model training, serving, and monitoring.
  • Leverage cloud computing resources and containerization technologies.

Preferred Education

Bachelor's Degree is required.

Required Technical And Professional Expertise

  • Strong background in artificial intelligence and machine learning including deep learning and LLMs.
  • Proficient in Python, PyTorch, Go, and C++.
  • Experienced with containerization tools like Docker and Kubernetes.

Preferred Technical And Professional Experience

  • Experience in developing AI platforms and infrastructure.
  • Familiarity with AI software stack components: data ingestion, training, serving, and monitoring.
  • Experience with cloud-based AI platforms and contributions to open source projects.

Key skills/competency

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning
  • Python
  • PyTorch
  • Go
  • C++
  • Docker
  • Kubernetes
  • Cloud Computing

How to Get Hired at IBM

🎯 Tips for Getting Hired

  • Research IBM's culture: Understand IBM mission, vision, and tech trends.
  • Tailor your resume: Highlight AI and software skills clearly.
  • Showcase project experience: Detail AI and ML projects effectively.
  • Prepare for technical interviews: Practice coding and system design questions.

📝 Interview Preparation Advice

Technical Preparation

Review AI algorithms and LLMs.
Practice Python and C++ coding.
Study containerization with Docker and Kubernetes.
Familiarize with cloud computing concepts.

Behavioral Questions

Describe a challenging technical project.
Explain your teamwork experience.
Discuss handling tight deadlines.
Share problem-solving approach.