Quality Lead, YouTube Global Vendor Operations
Google · Hyderabad · onsite · Posted 38d ago
Not disclosed5–7 yrsfulltimeSourced from
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About this role
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Hyderabad, Telangana, India; Bengaluru, Karnataka, India; Gurgaon, Haryana, India.Minimum qualifications:
The YouTube Global Vendor Operations team develops practical ways to address some of the most complex business issues to keep YouTube growing. We anticipate how decisions are made, persistently explore and uncover the business needs of our key clients, and understand how our range of product and service offerings can enable their business success.
We lead the global vendor quality program which enables our suppliers to deliver amazing experiences for YouTube's consumers, creators and partners.
Responsibilities
- Bachelor's degree or equivalent practical experience.
- 5 years of experience identifying process issues or working with cross-functional teams.
- 5 years of experience with contact center metrics and QA methodologies.
- Experience with LLM-based evaluation frameworks and prompt engineering.
- Experience in stratified sampling to identify specific interactions for human validation, ensuring that human efforts are focused on high-signal or ambiguous cases.
- Familiarity with QA automation and sampling tools.
The YouTube Global Vendor Operations team develops practical ways to address some of the most complex business issues to keep YouTube growing. We anticipate how decisions are made, persistently explore and uncover the business needs of our key clients, and understand how our range of product and service offerings can enable their business success.
We lead the global vendor quality program which enables our suppliers to deliver amazing experiences for YouTube's consumers, creators and partners.
Responsibilities
- Design Quality Lead (QA) Frameworks to ensure alignment with key business outcomes. Monitor AI QA model performance and accuracy using metrics such as Precision, Recall, and Human Inter-rater Agreement.
- Manage the ingestion of human-validated Golden Labeling from manual overrides to drive continuous, high-fidelity model retraining.
- Implement blind review protocols and complex multi-tiered human + AI arbitration to prevent Automation Bias.
- Design stratified sampling methodologies that prioritize high-risk or ambiguous interactions (e.g., cases where AI confidence is low) for human review.
- Partner with the training team to remediate quality gaps.
Sourced from foundit
Skills
LLM-based evaluation frameworkssampling toolsPrompt EngineeringQA AutomationQA Methodologiescontact center metricsstratified sampling
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