AI/ML Engineering Internship - weloglobal

Welocalize, a Welo Global brand, serves localization teams through AI-enabled multilingual content solutions that enable enterprises to operate and scale globally. Welocalize combines AI, automation, and human expertise to support enterprises in more than 300 languages, enabling accurate, culturally aligned, and compliant multilingual content at scale.

Welocalize’s Opal Platform comprises patented technology designed to automate multilingual content and improve workflow performance for enterprises. Opal functions as an agentic system that orchestrates AI, automation, and human expertise to coordinate content workflows across the lifecycle, improving speed, scalability, and operational control for global enterprises. These solutions operate within a secure and compliant environment supported by seven ISO certifications. Welocalize is headquartered in New York with offices worldwide. welocalize.com

MAIN PURPOSE OF JOB

The AI/ML Engineering Intern assists with the AI team to design, prototype, and deploy AI solutions that enhance Welocalize’s localization and business-workflow products. They contribute code, experiments, and ideas while gaining hands-on experience with cloud infrastructure and production best-practices, supported by dedicated mentors.

This is a 6 month, fixed term contract, with the possibility of extension.

MAIN DUTIES

• Assist in well-defined pieces of work around research & development. Contribute to model and algorithm design using state of the art AI techniques such as large-language-models (LLM).
• Contribute to rigorous evaluation of AI models and systems. Choose the appropriate metrics for the assigned task.
• Support the setup of reproducible experiments in Python, following best processes for experimental tracking
• Assist with tasks like data cleaning, feature engineering, and building baseline AI models.
• Contribute to documentation by maintaining concise experiment logs, clear code comments, and short write-ups.
• Help the team stay up to date by reading recent AI-related papers or exploring new tools, and summarizing key insights.
• Participate in internal demos, team discussions, and code reviews to gain experience and contribute where possible.

Success Indicators

• Learning Curve & Initiative: Willingness to learn. Demonstrate skill growth and ownership of small tasks from start to finish.
• Code Quality & Reproducibility: Well-structured, testable Python code and clearly documented experiments.
• Collaboration: Timely communication of progress and blockers. Thorough documentation of deliverables.
• Impactful Contributions: Measurable improvements in model accuracy, runtime efficiency, or tooling.

REQUIREMENTS

Education