About Abaka AI

Abaka AI is built on one mission: to be the world’s most trusted data partner for AI companies. More than 1,000 industry leaders across Generative AI, Embodied AI, and Automotive AI rely on us to power their data pipelines. With our headquarters in Silicon Valley—and teams in Paris, Singapore, and Tokyo—we support global partners with fast, reliable, and scalable data solutions.

Our offerings include a diverse catalog of off-the-shelf datasets (image, video, multimodal, reasoning, 3D, and beyond) as well as comprehensive data collection and annotation services. Whether teams need raw data, curated datasets, or full-cycle data engineering, Abaka AI provides the foundation for building high-performance AI systems.

About the Role

As a Quality Project Associate at Abaka AI, you will help build and scale the quality systems that power our global AI data operations. This is a highly cross-functional role focused on improving data quality, reviewer alignment, fraud prevention, and operational compliance across large-scale AI data annotation programs.

You will work closely with Project Managers, Operations teams, Reviewers, and Leadership to identify quality risks, investigate root causes, and develop scalable solutions that improve project outcomes. Rather than simply auditing completed work, you will help design the systems, processes, and governance frameworks that drive quality at scale.

This is a high-impact role at the intersection of quality assurance, crowdsourcing operations, trust & safety, and project management.

Responsibilities

Build and improve quality assurance and compliance systems across AI data annotation projects
Design quality standards, review processes, escalation workflows, and operational governance frameworks
Develop quality metrics, auditing methodologies, reviewer calibration programs, and random inspection systems
Monitor quality risks across large-scale annotator and reviewer pipelines
Identify and mitigate fraud, abuse, and quality risks, including multi-accounting, VPN/proxy usage, AI-generated responses, and low-quality contributors
Investigate root causes behind quality issues such as declining acceptance rates, reviewer misalignment, annotation quality degradation, workflow inefficiencies, and client requirement mismatches
Develop corrective actions and scalable solutions that improve project quality and customer acceptance rates
Improve reviewer consistency, accountability, and operational traceability across projects
Collaborate cross-functionally with Project Managers, Operations, Product, QA teams, and Leadership to drive quality initiatives
Support the development of scalable systems and processes that improve quality outcomes without increasing operational overhead
Contribute to 0→1 initiatives that strengthen quality management and operational excellence across the organization

Qualifications

Strong operational foundation in quality assurance, crowdsourcing operations, trust & safety, compliance operations, project operations, or related fields
Experience identifying and solving operational problems through process design, governance frameworks, or quality systems
Strong analytical thinking and root cause analysis capabilities
Understanding of crowdsourcing challenges such as reviewer inconsistency, contributor quality management, fraud prevention, and operational scalability
Ability to design scalable, traceable, and repeatable operational processes
High ownership mindset with the ability to operate independently in ambiguous environments
Strong written and verbal communication skills
Excellent stakeholder management and cross-functional collaboration abilities
Detail-oriented with a commitment to operational excellence
Interest in AI, machine learning, and large-scale data operations
Growth-oriented mindset with a bias toward continuous improvement and execution

Preferred Qualifications

Experience working at AI data platforms, crowdsourcing platforms, trust & safety organizations, or large-scale annotation operations
Experience managing reviewers, contributors, quality programs, or operational teams
Familiarity with quality dashboards, QA tooling, workflow management systems, or operational reporting platforms
Experience improving acceptance rates, quality metrics, or operational performance at scale
Startup or high-growth environment experience
Experience building quality systems and governance frameworks from 0→1
Familiarity with AI, LLM, data annotation, or human-in-the-loop workflows

Compensation & Benefits

The base salary range for this position is $70,000 - $120,000 USD annually.

Quality Project Associate

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Abaka AI