Assistant Engineer, Data Operations and Annotation

The Data Operations and Annotation Specialist supports the data lifecycle for Data Analytics (DA), Video Analytics (VA), Computer Vision (CV) and Machine Learning (ML) projects. The role collects, organizes, labels and quality-checks UAS and sensor datasets, ensuring that data is complete, traceable and ready for model development, testing and validation.

• Support the collection and ingestion of image, video, telemetry, sensor, mission and test data from UAS platforms, ground control systems and related equipment.
• Record and maintain essential metadata, including the data source, collection conditions, platform, sensor configuration, mission or test event and dataset status.
• Organize and maintain raw, processed, annotated, training, validation and test datasets using approved naming, storage, version-control and access procedures.
• Perform or coordinate data annotation using approved taxonomies and guidelines, including bounding boxes, polygons, masks, key points, class or event labels and timestamps.
• Conduct first-level checks for missing or corrupted files, incomplete metadata, duplicate samples, naming inconsistencies and annotation errors.
• Prepare and release traceable dataset packages for model training, evaluation, system testing, validation, demonstrations and engineering review.
• Maintain dataset registers, annotation status, version and release records, handover notes and known-issue logs.
• Support laboratory tests, flight tests, field trials and demonstrations where data collection or data handling is required.

• Working knowledge of file management, dataset organization, digital storage and spreadsheets or equivalent tracking tools.
• Ability to handle large image, video, telemetry or sensor datasets accurately and systematically.
• Familiarity with data annotation tools and common annotation methods such as boxes, polygons, masks, labels or event tags.
• Ability to follow technical instructions, naming conventions, annotation guidelines and data-handling procedures.
• Good attention to detail, organizational ability and communication skills.
• Ability to identify data-quality issues and escalate unclear or technically ambiguous cases to the relevant engineer.
• Willingness to support field tests, demonstrations and data-collection activities when required.

• Complete and traceable datasets with the required metadata.
• Consistently annotated and first-level quality-checked data.
• Up-to-date dataset, version, annotation and release records.
• Controlled dataset packages and handover information for engineering use.