Data Annotation Specialist

About the role

High quality annotations steer how robots understand and act in the world. Robotic arm precision and efficiency improve through carefully labeled video data. Engineering and research teams align on standards that shape how data is produced.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Video sequences receive manual annotations for boxes, masks, and keypoints. Each sequence gets tracked IDs and labeled actions plus temporal segments so robot behaviors are clearly defined. Guidelines are followed and quality checks are run to resolve ambiguities and fix errors that would degrade model performance. Model prelabels are validated and corrected, and auto-tracking and segmentation pipelines are tuned using insights from the review work. Throughput increases as pre-annotation and autolabeling tools are used to support the annotation workflow.

Requirements

An Associate's or Bachelor's degree (or equivalent experience) is required for this position. Consistent application of annotation guidelines appears in detail oriented work across tasks. Instructions are followed and independent work continues with minimal supervision. Written communication stays clear and collaboration with engineering and research remains effective.

Nice to have

Hands on experience annotating video with boxes, masks, keypoints, action labels, and ID tracking is valued. Comfort with annotation tools and with pre-annotation or autolabel review and correction supports accuracy. Familiarity with QA practices such as inter-annotator agreement, spot checks, and golden sets helps maintain reliability. Knowledge of common annotation formats like COCO, YOLO, MOT, and KITTI plus basic video concepts such as frame rate and codecs is beneficial.

Practical notes

This role operates under contract and is based in Redwood City. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

General video annotation work trains models to perceive scenes and moving objects. Common tools used in this field include labeling interfaces and tracking utilities. Quality assurance methods keep datasets reliable and consistent across large volumes. This role focuses on structured data tasks that directly influence robotic behavior in varied settings. Clear communication and attention to detail keep annotations aligned with model goals.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.