Remote AI Engineer Intern: GenAI & ML Projects

Celestica; San Jose (CA); Remote; USD 30,000 - 41,000; Full time

Benefits offered by this job

Time Off to Volunteer (2 paid days/yr)

Development programs and mentorship

Networking & intern events

Casual/remote-friendly environment

Job summary

Celestica is seeking a Student Intern - AI Engineer for a 3-month internship. You will design and implement agentic solutions and ML models, collaborating with cross-functional teams to drive projects from concept to delivery.

This remote opportunity supports May/June 2027 start, with exposure to GenAI technologies and hands-on experience in AI engineering. You will gain experience in data science techniques, model evaluation, and working with real-world data while contributing to ongoing

Qualifications

• Strong proficiency in Python and core software development principles.
• Foundational knowledge in Data Science and Machine Learning (data analysis, model evaluation, predictive modeling).
• Familiarity with Generative AI concepts, large language models, and autonomous agent architectures.

Responsibilities

• Design and implement agentic solutions and ML models.
• Collaborate with cross-functional teams to drive projects from concept to delivery.
• Apply data science techniques to analyze datasets and optimize model performance.
• Conduct experimental testing and benchmarking.
• Present findings and prototypes to stakeholders.

Skills

Python

Data Science

Generative AI

Ownership & accountability

Cross-functional collaboration

Education

Engineering, Computer Science, or Mathematics program

Tools

None

Job description

Celestica is seeking a Student Intern - AI Engineer for a 3-month internship. You will design and implement agentic solutions and ML models, collaborating with cross-functional teams to drive projects from concept to delivery.

This remote opportunity supports May/June 2027 start, with exposure to GenAI technologies and hands-on experience in AI engineering. You will gain experience in data science techniques, model evaluation, and working with real-world data while contributing to ongoing