Internship - Annotation Acceleration Job in Apple

Internship Annotation Acceleration

Heidelberg, BW, DE, Germany

Job Description

Application Deadline: Sunday 16th November Play a part in building the next revolution of machine learning technology. We’re looking for passionate researchers in the final years of their post-graduate studies to work on high-reaching curiosity-driven research projects that will impact the future of Apple, and our products, through applied machine learning. In this role, you’ll have the opportunity to work on innovative solutions in the space of data annotation. As a member of the team, you will be passionate about a variety of exciting problems, collaborate with extraordinary software and machine learning engineers to develop and implement intelligent solutions to improve our data annotation processes.

Description

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.

Minimum Qualifications

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.)

Preferred Qualifications

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.)

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