Real-World Data Collection & Scraping Engineer

Job description

You will build the data infrastructure that feeds our robotics and ML systems — collecting, labelling, and processing real-world sensor data from physical deployments, and building automated data scraping pipelines for training dataset construction at scale. WorkSprout is a deep-tech engineering and design partner across twelve practice areas — TinyML & Edge AI, Custom AI Development, AI Automation & Agents, Data Engineering & Collection, IoT Solutions, Hardware Prototyping, Robotics & Automation, 3D Design & Modeling, Software Development, Growth & DevOps, Branding & Creative Design, and Startup & Product Launch. We deliver systems that work in production, not just in demos. Office hours 08:00 – 17:00 BST, Monday to Friday; hybrid in Rajshahi, Bangladesh. Applications are reviewed on a rolling basis. To apply, email careers@worksprout.us with the subject "Real-World Data Collection & Scraping Engineer".

Responsibilities

• Design and deploy data collection rigs on physical robot platforms in the field
• Build automated web and API scraping pipelines for robotics dataset construction
• Process and annotate image, video, and sensor data for ML model training
• Develop data quality validation, deduplication, and versioning pipelines
• Manage dataset storage and versioning with DVC or equivalent tooling
• Coordinate field data collection sessions with robotics and hardware teams

Requirements

• Python proficiency with data processing libraries (Pandas, NumPy, Dask)
• Experience with web scraping frameworks (Scrapy, Playwright, BeautifulSoup)
• Knowledge of data annotation tools (Label Studio, CVAT, Roboflow)
• Familiarity with large-scale storage and dataset management practices
• Understanding of data privacy regulations and responsible scraping ethics
• Nice to have: Experience with ROS 2 bag files and sensor data serialisation formats
• Nice to have: Knowledge of distributed scraping with proxy rotation and rate limiting
• Nice to have: Background in active learning or semi-supervised annotation workflows

Benefits

• Work in the field collecting real-world robotics training data
• Competitive salary benchmarked to international rates
• Access to robotics lab and hardware deployment environments
• Dedicated research time for data pipeline innovation
• Open-source dataset contribution and publication support