Data Annotation Specialist

Join us to shape the next frontier of AI-driven robotics!

Dyna Robotics makes general-purpose robots powered by a proprietary embodied AI foundation model that generalizes and self-improves across varied environments with commercial-grade performance. Dyna's robots have been deployed at customers across multiple industries. Its frontier model has the top generalization and performance in the industry.

Dyna Robotics was founded by repeat founders Lindon Gao and York Yang, who sold Caper AI for $350 million, and former DeepMind research scientist Jason Ma. The company has raised over $140M, backed by top investors, including CRV and First Round. We're positioned to redefine the landscape of robotic automation. Join us to shape the next frontier of AI-driven robotics.

The Role:

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.

What You'll Do

• 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

What You'll Bring

• 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

Bonus Points For

• 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)