AI Quality Assurance Expert

Lead Quality Assurance Engineer

We are seeking a seasoned Lead Quality Assurance Engineer to join our team. The successful candidate will be responsible for architecting and upholding high-quality annotation workflows to ensure the integrity and accuracy of testing, training, and evaluation data for AI models.

About the Role

This is an exciting opportunity to work on first-of-its-kind problems at the frontier of AI safety. As a Lead Quality Assurance Engineer, you will lead a high-performing team of Data Quality Analysts, setting clear quality goals, delivering feedback, and fostering a culture of precision and accountability.

Key Responsibilities

• Build and guide a high-performing team: Lead and mentor a team of Data Quality Analysts, setting clear quality goals, delivering feedback, and fostering a culture of precision and accountability.
• Ensure quality at scale: Develop and continually refine robust QA processes, SOPs, and statistical quality metrics (e.g., F1 score, inter-annotator agreement) to protect the integrity of annotation outputs.
• Drive transparency and insight: Create dashboards and reports that reveal quality trends, root causes of errors, and improvement opportunities - communicating these insights to leadership and clients.
• Champion tool innovation and efficiency: Manage annotation and QA platforms (like Labelbox, Dataloop, LabelStudio), and lead the evaluation or implementation of new automation tools to elevate efficiency and maintain quality.

Requirements

• 3+ years of experience in data quality management, data operations, or related roles within AI/ML or data annotation environments.
• Proven track record in designing and executing quality assurance strategies for large-scale, multi-modal data annotation projects.
• Proven track record in a leadership role managing and developing high-performing, remote or distributed teams.
• Deep understanding of data annotation processes, quality assurance methodologies, and statistical quality metrics (e.g., F1 score, inter-annotator agreement).
• Strong data-analysis skills, with the ability to interrogate large datasets, perform statistical analyses, and translate findings into actionable recommendations.
• Excellent communication skills, with experience presenting complex data and quality insights to technical and non-technical stakeholders.
• Proficiency with annotation and QA tools (e.g., Labelbox, Dataloop, LabelStudio).
• High-level of proficiency in common data-analysis tools, such as Excel and Google Sheets.
• Familiarity with programmatic data analysis techniques (e.g., Python, SQL).
• Familiarity with the core concepts of AI/ML pipelines, including data preparation, model training, and evaluation.

Preferred Qualifications

• Prior experience in an agile or fast-paced tech environment with exposure to AI/ML pipelines.
• Experience in a managed services or vendor-driven environment.
• Familiarity with prompt engineering and large-language-model assisted workflows to optimize annotation and validation processes.
• In-depth knowledge of ethical AI practices and compliance frameworks.

Why You'll Love This Role

• Remote-first flexibility: Work from anywhere you're most productive.
• Every challenge is new: Work on first-of-its-kind problems at the frontier of AI safety.
• Visible impact: Your architectures directly influence how the world's leading AI teams mitigate risk.
• Room to grow: Build the solutions function from scratch and scale into leadership as revenue follows.
• Mission-driven team: Join sellers, researchers, and engineers united by a passion for safer AI.

What We Offer

We prioritize our employees' well-being by offering competitive industry salaries and comprehensive benefits packages. Our commitment to a People First culture is reflected in the various departments we have established, including Total Rewards, Wellness, HR, and Diversity. We take pride in our inclusive environment and positive impact on the community.

How To Apply

Kindly submit your application through our website.

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