Data Annotation Lead

About the role

This position defines annotation strategy for home robotics and leads a data annotation team. It balances speed, quality, and task variety while aligning research experiments with product needs.

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

Data annotation frameworks are shaped alongside Machine Learning, Software engineering, and Data teams to set operational direction. Annotation practices are standardized to maintain consistency and quality across collection efforts. Guidance and feedback flow to data annotators so their work aligns with quality expectations and a culture of engagement. Documentation for annotation tasks and workflows is produced to standardize practices and support scaling. New design phases include team annotations to surface challenges and refine requirements early. Processes for data annotation are created and refined to handle new experiment needs while managing quality and throughput. External partners are selected and managed where in house capacity does not cover annotation needs.

Requirements

A degree is required for the role. Clear written and verbal communication guides data annotators and stakeholders effectively. The role handles unexpected challenges while coordinating multiple stakeholders. Key stakeholders are engaged to remove blockers for data annotators. Competing requests are prioritized so the team moves quickly while staying organized. A respectful leadership style treats people as people while driving annotation outcomes. An intermediate understanding of ML concepts supports alignment with model needs.

Nice to have

Previous experience leading data annotation teams is valued. A history of top tier annotation performance is considered a strong signal. Technical skills to build data annotation tools are appreciated. Experience using AI to improve annotation productivity is noted.

Skills & tools

Annotation platforms and tooling structure and track work. ML concepts and data workflows inform annotation design and quality checks.

Practical notes

The role is based in Redwood City, with onsite work expectations defined by policy. Travel may be required within the role depending on project needs. 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

Roles in robotics often combine operations and technical collaboration. Annotation work connects directly to model performance and data quality. Clear processes and communication help teams scale efficiently. Hands on annotation keeps the team aligned with real world data challenges. Leadership in data teams balances people management with delivery outcomes.

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.