Full-Time QA Strategist
Pareto Ai is hiring a remote Full-Time QA Strategist. The career level for this job opening is Experienced and is accepting USA based applicants remotely. Read complete job description before applying.
Pareto Ai
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About us
At Pareto.AI, we’re on a mission to enable top talent around the world to participate in the development of cutting-edge AI models.
In coming years, AI models will transform how we work and create thousands of new AI training jobs for skilled talent around the world. We’ve joined forces with top AI and crowd researchers at Anthropic, Character.AI, Imbue, Stanford, and University of Pennsylvania to build a fair and ethical platform for AI developers to collaborate with domain experts to train bespoke AI models.
Context
To strengthen and standardize our approach to data quality at scale, we propose creating a QA Strategist role under the QA Lead. This position will help ensure our QA practices grow with the organization, adapt effectively to increasing project volume and evolving demands, and consistently deliver high-quality, client-aligned datasets.
Role Overview
The QA Strategist is a full-time role reporting to the QA Lead. This position will focus on the design, implementation, and validation of quality assurance processes across projects, with the goal of improving data reliability, consistency, and efficiency. The position will also provide flexible support on quality-related tasks as needs arise.
Core Responsibilities
- Support the QA Lead in developing and documenting QA standards and best practices.
- Ensure QA processes are applied consistently across projects.
- Help identify recurring data quality issues and propose solutions.
- Provide guidance and support to project teams on QA methods.
- Collaborate with internal stakeholders and external partners to align on quality expectations and processes.
- Assist in preparing quality-related updates and reports for clients and leadership.
- Conduct quality reviews of the external Expert Reviewer pool when needed to ensure alignment with established standards.
- Provide flexible support on quality-related tasks to address immediate priorities.
Expected Impact
- Improved accuracy and reliability of AI training datasets.
- Consistent QA standards and processes across projects.
- Reduction in recurring systemic quality issues.
- Scalable QA processes to support future growth.