2,268 jobs for "Product Quality Engineer"

Responsibilities

Team Introduction
Safety Model Operations (SMO) is a dedicated organization that provides end-to-end support for AI Moderation services across a wide range of international products. Our core function is to deliver high-quality labeling, evaluation, and safety-related data services that help ensure AI systems operate responsibly, accurately, and in alignment with product requirements.

The SMO Delivery Team plays a critical role in the organization. Its primary responsibility is to carry out the full spectrum of quality assurance activities for each project. This includes:
- Conducting detailed reviews and complex RCA's to ensure labeling accuracy and consistency
- Monitoring quality performance and compliance against project-specific KPIs
- Identifying trends, risks, and potential gaps in processes or guidelines
- Providing structured feedback and improvement recommendations to the Central Project Team
- Supporting continual optimization of workflows, tools, and evaluation methodologies
- Improve Model performance of AI models

What will I do?
As Group Leader, you will lead and scale large operational teams within Safety Model Operations across APAC. You will drive operational excellence while ensuring teams deeply understand the link between high-quality human operations (content moderation, data labeling, red-teaming, and evaluation) and improved AI model performance. This role involves coaching team leads, managing cross-functional stakeholders, and actively collaborating with product, engineering, and policy teams to optimize annotation workflows and close the loop between operations and model outcomes.

Responsibilities
- Lead, motivate, and develop large, fast-paced operational teams to deliver high-quality AI moderation and labeling services, with clear accountability for both operational KPIs and downstream model impact.
- Champion a strong understanding of how operations (human content moderation, annotation quality, guideline design, and rater performance) affect model training, safety, and alignment - including RLHF/RLAIF feedback loops, bias introduction, and safety regressions.
- Conduct detailed reviews, complex root cause analyses (RCAs), and quality audits while linking findings to model-level outcomes (e.g., safety benchmark improvements, reduced hallucinations, or lower post-deployment incidents).
- Monitor quality performance and compliance against project KPIs; proactively identify trends, risks, and gaps in processes, guidelines, or rater pools that could degrade model performance.
- Drive continuous improvement initiatives focused on annotation accuracy, inter-annotator agreement, cultural/contextual sensitivity, and the translation of human feedback into better model generalization.
- Collaborate cross-functionally with product, engineering, policy, and data science teams to run experiments, measure the impact of operational changes on model metrics, and enhance proprietary tools, systems, and workflows.
- Build and maintain training programs that equip teams with both operational excellence and a systems-thinking mindset on data-model feedback loops.
- Manage data collection, reporting, productivity, unit costs, and quality targets while ensuring exceptional delivery and regulatory compliance.
- Identify, escalate, and resolve operational issues, removing barriers and driving alignment across stakeholders.

Qualifications

Minimum Qualifications:
- Bachelor's degree in business, management, operations, data science, or a related field. Minimum 3 years of leadership experience managing managers, ideally in Trust & Safety, AI Operations, Data Annotation, or Tech Operations, with a proven track record of scaling high-performing teams.
- Strong understanding of the impact of human operations (content moderation, data labeling, QA processes) on AI model training and performance, including feedback loops, data quality issues, and their effect on safety/alignment.
- Demonstrated experience in strategy development, operational execution, and data-driven decision making under tight timelines and evolving requirements.
- Excellent communication and stakeholder management skills, with the ability to influence and collaborate effectively across engineering, product, policy, and operations teams.
- Strong analytical and problem-solving skills; experience interpreting complex operational data and linking it to model-level outcomes.
- Proficiency with data tools for operational analysis, including SQL and data visualization platforms (e.g., Power BI) to monitor quality metrics, Inter-Annotator Agreement (IAA), audit results, and performance trends.
- Solid understanding of machine learning workflows, data annotation, model evaluation, and continuous improvement cycles between operations and model training.

Preferred Qualifications:
- Hands-on experience building and scaling large teams in high-growth AI or tech environments, particularly with global rater pools and multilingual/cultural moderation challenges.
- Demonstrated success in cross-functional projects that improved model performance through better data operations, guideline design, or quality control mechanisms.
- Experience working in a highly matrixed, global organization with external partners.
- Familiarity with modern AI alignment techniques (RLHF, RLAIF, preference modeling, red-teaming) and scalable oversight concepts.

Responsibilities

Qualifications

Qualifications

Minimum Qualifications:
- Bachelor's degree in business, management, operations, data science, or a related field. Minimum 3 years of leadership experience managing managers, ideally in Trust & Safety, AI Operations, Data Annotation, or Tech Operations, with a proven track record of scaling high-performing teams.
- Strong understanding of the impact of human operations (content moderation, data labeling, QA processes) on AI model training and performance, including feedback loops, data quality issues, and their effect on safety/alignment.
- Demonstrated experience in strategy development, operational execution, and data-driven decision making under tight timelines and evolving requirements.
- Excellent communication and stakeholder management skills, with the ability to influence and collaborate effectively across engineering, product, policy, and operations teams.
- Strong analytical and problem-solving skills; experience interpreting complex operational data and linking it to model-level outcomes.
- Proficiency with data tools for operational analysis, including SQL and data visualization platforms (e.g., Power BI) to monitor quality metrics, Inter-Annotator Agreement (IAA), audit results, and performance trends.
- Solid understanding of machine learning workflows, data annotation, model evaluation, and continuous improvement cycles between operations and model training.

Preferred Qualifications:
- Hands-on experience building and scaling large teams in high-growth AI or tech environments, particularly with global rater pools and multilingual/cultural moderation challenges.
- Demonstrated success in cross-functional projects that improved model performance through better data operations, guideline design, or quality control mechanisms.
- Experience working in a highly matrixed, global organization with external partners.
- Familiarity with modern AI alignment techniques (RLHF, RLAIF, preference modeling, red-teaming) and scalable oversight concepts.

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