AI ML Engineering Internship
3 days ago
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
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. 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.
- Education
- Completed or actively pursuing a BSc or MSc in Computer Science, Data Science, AI, or a related field (final-year undergraduates welcome).
- Technical Foundation
- Coursework or personal projects in AI, machine learning or NLP, solid Python fundamentals, hands on experience with LLMs
- Tools & Frameworks
- Familiarity with at least one AI/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.
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