Director of Data Quality, Gen AI

Labelbox is the data factory for generative AI, providing the highest quality training data for frontier and task-specific models. Labelbox's comprehensive platform combines on-demand labeling services with the industry-leading data labeling platform. The Boost labeling service is powered by the Alignerr community of highly-educated experts, who span all major languages and a diverse range of advanced subjects. They are available on-demand to rapidly generate new data for supervised fine-tuning, RLHF, and more. Labelbox's software-first approach delivers unmatched control and transparency into the labeling process, leading to the generation of high-quality, consistent data at scale.

Labelbox is backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins. Our customers include Fortune 500 enterprises and leading AI labs.

Your Day to Day

• Recruit, train, and mentor a high-performing QA team.
• Foster a culture of excellence, accountability, and continuous learning.
• Design, implement, and optimize QA policies and procedures.
• Apply advanced QC/QA methods, such as consensus scoring and statistical process control, to ensure quality standards. Identify workflow inefficiencies and implement best practices to enhance efficiency and reduce errors.
• Drive initiatives for process improvements and automation in QA.
• Develop long-term quality strategies aligned with company goals and industry trends.
• Align QA objectives with overall business strategy.
• Work with operations, product, engineering, and client teams to optimize annotation guidelines and processes.
• Represent the QA function in client meetings and strategy sessions. Analyze quality metrics and KPIs to drive decision-making.
• Prepare and present detailed reports to senior management.
• Maintain compliance with industry standards and regulatory requirements. Stay updated on advancements in QA technologies and methodologies.

About You

• Bachelor's or Master's degree in Data Science, Computer Science, Quality Management, or a related field.
• Minimum of 5 years in quality management, particularly in data annotation or related industries.
• Proven track record of leading QA teams and scaling processes in fast-paced environments.
• Expert knowledge of QC/QA methods, including consensus scoring, Inter-Rater Reliability (IRR), and statistical process control.
• Familiarity with data annotation tools, machine learning workflows, and statistical analysis software.
• Experience with automation and AI-driven quality assurance tools is a plus.
• Certifications such as Six Sigma Black Belt or ISO Quality Management are highly desirable.
• Exceptional leadership, team-building, and communication skills.
• Strong problem-solving and analytical abilities, with an innovative and data-driven mindset.
• High adaptability and effectiveness in a fast-paced, client-centric environment.

Labelbox strives to ensure pay parity across the organization and discuss compensation transparently. The expected annual base salary range for United States-based candidates is below. This range is not inclusive of any potential equity packages or additional benefits. Exact compensation varies based on a variety of factors, including skills and competencies, experience, and geographical location.

Annual base salary range

$195,000-$230,000 USD

Excel in a remote-friendly hybrid model.

We are dedicated to achieving excellence and recognize the importance of bringing our talented team together. While we continue to embrace remote work, we have transitioned to a hybrid model with a focus on nurturing collaboration and connection within our dedicated tech hubs in the San Francisco Bay Area, New York City Metro Area, and Wrocław, Poland. We encourage asynchronous communication, autonomy, and ownership of tasks, with the added convenience of hub-based gatherings.

Your Personal Data Privacy: Any personal information you provide Labelbox as a part of your application will be processed in accordance with Labelbox's Job Applicant Privacy notice.

Any emails from Labelbox team members will originate from a @labelbox.com email address. If you encounter anything that raises suspicions during your interactions, we encourage you to exercise caution and suspend or discontinue communications.

Location: Hayes Valley, CA, US

Posted Date: 12/22/2024