Data Annotator

About the Role We are partnering with a leading AI company that builds advanced models using high-quality, well-structured data. As part of this work, we are seeking a Data Annotation Specialist - Infotainment Logs with experience reviewing automotive infotainment system logs. In this role, you will review and tag infotainment device logs to help create clean, accurately labeled datasets that strengthen downstream model training, analytics, and engineering workflows. The ideal contractor has prior technical labeling experience, strong attention to detail, and familiarity with embedded or automotive system logs. This role is focused on data review, annotation, and quality assurance, not software engineering or system development. As a Data Annotation Specialist - Infotainment Logs, you will: • Review infotainment device logs and extract key data points, events, and system behaviors. • Apply structured labels, tags, and metadata to log output using established taxonomies and annotation guidelines. • Identify issues such as warnings, errors, performance abnormalities, connectivity interruptions, media playback anomalies, or navigation failures. • Document patterns and edge cases to support engineering, QA, and model-training teams. Ideal candidate profile Must have previous experience in infotainment logs and can work independently Daily tasks In this role, you will review and tag infotainment device logs to help create clean, accurately labeled datasets that strengthen downstream model training, analytics, and engineering workflows. The ideal contractor has prior technical labeling experience, strong attention to detail, and familiarity with embedded or automotive system logs. Review infotainment device logs and extract key data points, events, and system behaviors. Apply structured labels, tags, and metadata to log output using established taxonomies and annotation guidelines. Identify issues such as warnings, errors, performance abnormalities, connectivity interruptions, media playback anomalies, or navigation failures. Document patterns and edge cases to support engineering, QA, and model-training teams. Maintain high accuracy and consistency across labeling tasks. Provide feedback to improve annotation guidelines, decision trees, and tooling. Work independently and manage your task queue in a deadline-driven environment. Required skills Experience in data labeling, annotation, or training data QA, ideally for technical or system-level datasets. CAN Protocol Expertise is most critical skill. The CAN Bus is the fundamental communication network within all modern cars, including the iDrive system. Experience with CAN, I2C, and SPI means they understand vehicle communication at a deep level. Familiarity with automotive infotainment logs, mobile/embedded system logs, or Android/Linux log structures. Comfortable interpreting large, unstructured log files (e.g., logcat output, system traces, connectivity logs). Highly detail-oriented with excellent pattern-recognition skills. Strong written communication for summarizing findings and documenting anomalies. Experience with annotation platforms, labeling queues, or internal data tools. Prior experience supporting AI, ML, or data-centric engineering teams is highly valuable.