The posting
We are looking for detail-oriented Data Annotators to support the development and continuous improvement of our advanced robotics systems by reviewing, labeling, and validating operational data from robot activities.
This role is crucial in improving robot performance, accuracy, and machine learning capabilities through the precise annotation of images, videos, sensor data, and operational outputs. These annotated inputs enable machine learning models to learn from real-world scenarios, recognize patterns, and progressively enhance decision-making abilities, ultimately supporting the advancement toward greater robot autonomy.
Ideal candidates will possess strong analytical skills, excellent attention to detail, and the ability to work accurately within structured guidelines. This position requires consistency, focus, and the ability to quickly adapt to new technologies and annotation platforms in a fast-paced, tech-driven environment.
- Health Insurance/HMO
- Enjoy unlimited MadMax Coffee
- Diverse learning & growth opportunities
- Accessible Cloud HR platform (Sprout)
- Above standard leaves
- Review, label, and annotate robot-generated data across images, videos, system logs, and tele-operation recordings to ensure high-quality inputs for machine learning model training, including:
- Breaking down robot demonstration videos into structured steps by identifying the overall task, smaller sub-steps, and specific actions performed by the robot (e.g., picking up, moving, or placing objects); Providing accurate labels for robot actions and behaviors, including identifying the stage of the task within each sequence; Creating clear and accurate natural language descriptions of robot actions, behaviors, and the operational environment; Annotating visual data by drawing bounding boxes, labeling object types, and tracking objects across video frames;
- Accurately identify objects, movements, patterns, and environmental factors to support AI training and machine learning model development;
- Utilize specialized annotation tools, platforms, and data management systems to complete tasks efficiently and in accordance with defined standards;
- Ensure high-quality outputs by following annotation guidelines and performing self-checks, quality assurance reviews, and validations on completed work;
- Identify edge cases, operational inconsistencies, ambiguities, and robot failure modes, and escalate issues with clear and actionable feedback for review and correction;
- Collaborate with Operations Analysts, Team Leads, and US-based counterparts, including engineering and technical teams, to improve annotation workflows and optimize robot learning processes;
- Maintain accurate documentation of completed tasks, issues, and observations for tracking and reporting purposes;
- Adhere to strict data security, confidentiality, and compliance standards to protect operational and client information;
- Participate in training sessions and continuously adapt to updated tools, workflows, and project requirements;
- Strictly follow workplace safety, operational procedures, and company policies to maintain a secure working environment.
- Aptitude: Comfortable working with annotation platforms, software tools, spreadsheets, dashboards, and technology-driven systems.
- Familiarity with robotics, AI, or machine learning environments is an advantage.
- Attention to Detail: High level of accuracy and consistency in reviewing and labeling large volumes of data. Strong focus and ability to detect minor discrepancies; Capable of efficiently handling repetitive tasks while meeting deadlines and ensuring high-quality outputs.
- Analytical Skills: Ability to identify patterns, anomalies, and operational details while following strict annotation standards.
- Work Environment Fit: Ability to thrive in structured, high-tech operational environments.
- Communication Skills: Basic English proficiency is acceptable; however, candidates must be able to clearly understand instructions and accurately document and communicate annotations, observations, and issues in a structured manner. While the role is predominantly non-voice, effective written communication is essential to ensure alignment with guidelines, proper escalation of edge cases, and accurate support for machine learning and engineering teams.
- Problem-Solving: Ability to identify data inconsistencies and escalate issues efficiently and accurately.
- Other Essential Traits: Methodical, organized, focused, highly disciplined, patient, and capable of repetitive high-accuracy work. Familiarity with QA practices (inter-annotator agreement, spot checks, golden sets)
- Previous experience in data annotation, quality assurance, data processing, or back-office operations;
- Familiarity with robotics, automation platforms, AI training systems, or tele-operation environments;
- Experience working with image/video labeling, document validation, or structured data review;
- Basic understanding of machine learning workflows and annotation standards;
- Experience in gaming, simulation environments, or high-focus operational tasks is an advantage;
- Understanding of cybersecurity practices relevant to handling operational and sensitive system data.
