AI/ML Engineer Intern

Job Description

At IBM Software, we turn client challenges into solutions, building the world's leading AI-powered, cloud-native products that shape the future of business and society. Our legacy of innovation creates endless opportunities for IBMers to learn, grow, and make an impact on a global scale. Working in Software means joining a team fueled by curiosity and collaboration. You'll work with diverse technologies, partners, and industries to design, develop, and deliver solutions that power digital transformation. With a culture that values innovation, growth, and continuous learning, IBM Software places you at the heart of IBM's product and technology landscape. Here, you'll have the tools and opportunities to advance your career while creating software that changes the world.

IBM Automation and AI group is seeking an AI/ML Engineer intern specializing in machine learning and Large Language Models (LLMs) to join our AI/ML Center of Excellence team at the IBM Silicon Valley Lab location. In this role, you will collaborate with cross-functional product teams to implement cutting-edge AI/ML capabilities across IBM's Automation product portfolio. You will lead efforts in data collection for LLM training and evaluation, integrate LLM technologies into existing and new products, and establish best practices for AI/ML feature implementation with product teams.

What You'll Do

• Data Collection and Management for LLM Evaluation and Training
• LLM Integration and Implementation
• AI/ML Best Practices and Innovation

Program Dates

Internship start dates vary based on your academic calendar.

• Semester System: May 24, 2027 - August 13, 2027
• Quarter System: June 14, 2027 - September 3, 2027

Candidates must be available to participate for the full duration of the internship corresponding to their academic schedule. (Guidelines - transferable skills are encouraged)

Qualifications

• Pending Bachelor's or master's degree or higher in Computer Science, Machine Learning, AI, or related technical field
• Demonstrated knowledge of NLP and large language models including model evaluation and algorithm design
• Strong programming skills in Python and familiarity with ML frameworks (PyTorch, TensorFlow, or JAX)
• Experience with data processing pipelines and working with large datasets
• Knowledge of MLOps practices and tools for model deployment and monitoring
• Experience with fine-tuning and prompt engineering for LLMs
• Deep understanding of transformer architectures and attention mechanisms
• Proficiency in vector databases and embedding technologies
• Knowledge of model serving frameworks (TensorRT, ONNX, TorchServe)