(Part-Time) Data Annotator – AI Model Training
About The Position
Seyond is a leading global provider of image-grade LiDAR technology, powering a safer, smarter and more mobile world across the automotive, intelligent transportation, robotics and industrial automation sectors. Founded in Silicon Valley with strategically placed research and manufacturing facilities across the globe, Seyond is crafting LiDAR solutions that elevate autonomous driving and fuel the advancement of smart infrastructure development. Our dynamic portfolio – including ultra-long range flagship LiDAR sensor Falcon, mid-to-short range LiDAR sensor Robin and perception service software platform OmniVidi – powers automotive and ITS solutions for partners like NIO, Faraday Future, Exwayz and Hexagon. Currently, over 200,000 Falcon units are in use, and the product continues to be mass-produced today. Job Summary As a Data Annotator, you will play a key role in training and improving our AI models. You’ll be responsible for carefully preparing, slicing, and labeling LiDAR point cloud data, ensuring that every object is annotated accurately and consistently. This role is ideal for individuals who are detail-oriented, comfortable with structured tasks, and eager to contribute to cutting-edge AI development.
Requirements
- Technical skills: Comfortable with spreadsheets, MS Office, and browser-based software tools.
- Attention to detail: Able to spot small differences and inconsistencies in 3D data.
- Time management: Work efficiently while maintaining high accuracy.
- Diligence: Commit to consistent annotation work within set schedules.
- Communication: Able to document progress, report issues, and collaborate with colleagues clearly.
- Problem-solving skills: Proactively flag unusual data or system issues.
- Adaptability: Willingness to learn new tools and workflows quickly.
Nice To Haves
- Familiarity with LiDAR or 3D visualization tools.
Responsibilities
- Data Preparation: Upload raw data into our data management system.
- Organize and prepare datasets for annotation.
- Data Slicing: Slice point cloud data into frames based on provided timestamps.
- Transfer prepared data into the Smart Labeling System.
- Annotation & Classification: Assign object classes in 3D point cloud frames (e.g., car, bus, pedestrian).
- Adjust object bounding boxes, dimensions, and heading directions.
- Record progress and report completed frames for review.
- Review & Quality Check: Verify accuracy of annotated objects (class, size, heading).
- Submit finalized frames for model training.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Part-time
Career Level
Entry Level
Education Level
No Education Listed
