Your primary responsibility will be to research and experiment with the integration of ML models into the annotation process, with the goal of optimizing our solutions for efficiency. You will delve into effects like anomaly detection and annotation bias induced by ML models, contributing your findings to the improvement of our systems. This internship offers an outstanding chance to gain hands-on experience in applying machine learning to tackle real-world challenges in the field of data annotation. You will collaborate with a talented team of AIML professionals. To be considered for this role, you should have a strong background in machine learning and a passion for research and development. Proficiency in Python or other programming languages, along with experience in data analysis and experimentation, is crucial. Strong master’s or PhD student in Machine Learning or Computer Science with specialization in ML. Advanced understanding of classical or deep machine learning techniques. Fluency in Python and at least one lower level language (Go, Rust, C++, etc.) Specialization in at least one of the following topics: Active learning / optimal experimental design Uncertainty estimation in ML Models (e.g. sensitivity analysis, dropout based techniques, fisher information, etc.)