Data Annotation Specialist
Company Overview:
Dyna Robotics is at the forefront of revolutionizing robotic manipulation with cutting-edge foundation models. Our mission is to empower businesses by automating repetitive, stationary tasks with affordable, intelligent robotic arms. Leveraging the latest advancements in foundation models, we're driving the future of general-purpose robotics—one manipulation skill at a time.
Dyna Robotics was founded by industry leaders who previously achieved a $350 million exit in grocery deep tech as well as top robotics researchers from DeepMind and Nvidia. Our team blends world-class research, engineering, and product innovation to drive the future of robotic manipulation. With $20mil+ in funding, we're positioned to redefine the landscape of robotic automation. Join us to shape the next frontier of AI-driven robotics.
Position Overview:
As a Data Annotation Specialist at Dyna Robotics, you will be pivotal in iterating on our AI system by annotating data on various diverse tasks performed by robots. Your will directly influence the performance of our robotic arms, helping them become more accurate and efficient. You will work closely with our engineering and research teams, ensuring data is labeled of the highest quality and meets required standards.
Key Responsibilities:
Manually annotate video sequences (boxes/masks/keypoints), track IDs, and label actions & temporal segments
Maintain data integrity by applying guidelines and QC checks; resolve ambiguities and fix errors
Leverage pre-annotation/autolabeling tools to boost throughput—validate/correct model prelabels and tune auto-tracking/segmentation pipelines
Qualifications:
Associate’s or Bachelor’s degree (or equivalent experience)
Strong attention to detail; consistent application of guidelines
Ability to follow detailed instructions and work independently with minimal supervision
Clear written communication and a collaborative attitude
Preferred Qualifications:
Hands-on experience annotating video (boxes/masks/keypoints, action labels, ID tracking)
Proficiency with annotation tools; comfort with pre-annotation/autolabel review and correction
Familiarity with QA practices (inter-annotator agreement, spot checks, golden sets)
Knowledge of common annotation formats (e.g., COCO, YOLO, MOT/KITTI) and basic video concepts (frame rate, codecs)