ML Intern

Role overview Help improve the efficiency and quality of large language models by exploring model configurations and evaluating ways to reduce token use while preserving strong results. The work combines experimentation, data pipeline development, performance analysis, and contributions to tools that track model behavior.

Responsibilities - Test language model configurations and identify opportunities to improve efficiency. - Build and maintain data pipelines for model training and evaluation. - Analyze metrics to assess model quality and resource use. - Contribute to tools for monitoring and optimizing model behavior. - Follow relevant machine learning research and apply useful findings to product improvements.

Requirements - A degree in computer science, data science, engineering, mathematics, or a related field, or equivalent practical experience. - Hands-on experience building or training machine learning models in Python, using a framework such as PyTorch, TensorFlow, or scikit-learn. - Knowledge of machine learning, statistics, model evaluation, and optimization. - Familiarity with large language models, natural language processing, and prompt design or tuning. - Experience with machine learning data pipelines and performance analysis.

Nice to have - Experience building tools or monitoring systems to track model performance.

Benefits and work setup - Remote, full-time role.