AI ML Engineering Intern

If you have a Candidate Login already, but have forgotten your password please use the steps to reset your password. If you have forgotten your email login, please contact [[email protected]](mailto:[email protected]) subject Workday Candidate Login When creating your Workday account and entering personal information like name, address, please do not use ALL CAPS. Thank you! NOTICE:  For Privacy Policy please review [here](https://www.welocalize.com/security-compliance/privacy-policy/) **Job Responsibilities:** The AI/ML Engineering Intern assists with the AI/ML team to design, prototype, and deploy machine-learning 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. **Key Responsibilities** • Assist in well-defined pieces of work around research & development. Contribute model and algorithm design using state of the art machine learning techniques such as large-language-models (LLM). • Contribute to rigorous evaluation of ML 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 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 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** 1\. Learning Curve & Initiative: Willingness to learn. Demonstrate skill growth and ownership of small tasks from start to finish. 2\. Code Quality & Reproducibility: Well-structured, testable Python code and clearly documented experiments. 3\. Collaboration: Timely communication of progress and blockers. Thorough documentation of deliverables. 4\. Impactful Contributions: Measurable improvements in model accuracy, runtime efficiency, or tooling. **Minimum Qualifications** • Education: Completed or actively pursuing a BSc or MSc in Computer Science, Data Science, or a related field (final-year undergraduates welcome) • Technical Foundation: Coursework or personal projects in machine learning or NLP, solid Python fundamentals, hands on experience with LLMs • Tools & Frameworks: Familiarity with at least one ML library such as scikit-learn, TensorFlow, or PyTorch, experience with Git. Basic knowledge of Docker or cloud services is a plus. • Soft Skills: Clear written and verbal English communication, curiosity, problem-solving attitude, and willingness to ask questions. Additional Job Details: