Robotics Data Quality Lead

Welo Data works with technology companies to provide datasets that are high-quality, ethically sourced, relevant, diverse, and scalable to supercharge their AI models. As a Welocalize brand, WeloData leverages over 25 years of experience in partnering with the world’s most innovative companies and brings together a curated global community of over 500,000 AI training and domain experts to offer services that span:
ANNOTATION & LABELLING: Transcription, summarization, image and video classification and labeling. ENHANCING LLMs: Prompt engineering, SFT, RLHF, red teaming and adversarial model training, model output ranking. DATA COLLECTION & GENERATION: From institutional languages to remote field audio collection. RELEVANCE & INTENT: Culturally nuanced and aware, ranking, relevance, and evaluation to train models for search, ads, and LLM output.
Want to join our Welo Data team? We bring practical, applied AI expertise to projects. We have both strong academic experience and a deep working knowledge of state-of-the-art AI tools, frameworks, and best practices. Help us elevate our clients' Data at Welo Data.

The Robotics Data Quality Lead is responsible for defining, implementing, and continuously improving quality standards across all robotics data collection workflows. This role ensures that all collected data (video, sensor, teleoperation outputs, and annotations) meets client-defined accuracy, consistency, and usability requirements for AI model training.

Working closely with the Robotics Program Manager and Workflow Managers, this role drives quality strategy, establishes scalable QA frameworks, and serves as the primary point of contact for all quality-related matters with stakeholders.

 

Key Responsibilities

Quality Strategy & Ownership

• Define and own the end-to-end data quality strategy across robotics data collection and annotation workflows.
• Establish quality KPIs, SLAs, and acceptance criteria.
• Develop and track quality OKRs, ensuring alignment with overall program goals.
• Proactively identify risks and implement preventative quality measures.

Quality Operations & Execution

• Design and implement quality assurance frameworks, including audits, sampling methodologies, and validation processes.
• Oversee daily quality performance, ensuring consistency across operators, moderators, and workflows.
• Partner with Workflow Managers to ensure task instructions and execution align with quality standards.
• Ensure high-quality outputs across:
• Teleoperation sessions
• Robotics interaction data
• Sensor and video capture
• Annotation and labeling outputs

Performance Monitoring & Continuous Improvement

• Monitor and report on quality metrics (accuracy, consistency, error rates, throughput impact).
• Conduct root cause analysis (RCA) on quality issues and implement corrective and preventive actions (CAPA).
• Drive continuous improvement initiatives across tools, workflows, and training.
• Identify opportunities to improve efficiency without compromising quality.

Team Leadership & Enablement

• Lead and mentor a team of Robotics Quality Specialists.
• Define training programs and onboarding for new quality team members.
• Provide coaching and performance management for direct reports.
• Promote a quality-first culture across all operational layers.

Client & Stakeholder Engagement

• Serve as the primary escalation point for quality-related issues.
• Interface directly with stakeholders on:
• Quality performance
• Reporting and insights
• Escalations and resolution plans
• Prepare and present regular quality reports and business reviews.
• Anticipate client needs and proactively recommend improvements.

Process, Documentation & Compliance

• Maintain and evolve quality documentation, including guidelines, SOPs, and evaluation frameworks.
• Ensure alignment with safety and compliance standards in robotics environments.
• Support cross-functional collaboration with:
• Safety & Compliance teams
• Program leadership
• Engineering / client teams

Experience

• Experience managing quality in large-scale data operations, robotics, AI/ML, or similar environments.
• Proven ability to lead