Role Purpose
Appen's GenAI Project Delivery team sits at the intersection of AI development and real-world data quality, executing the annotation, evaluation, and data collection work that shapes how frontier models perform. The purpose of this role is to drive end-to-end delivery excellence across GenAI projects by ensuring task configuration, contributor performance, and data quality outcomes stay tightly aligned to client expectations. This is a hands-on, cross-functional role with influence over delivery standards, client outcomes, and the continuous improvement of how Appen executes at scale.
Your Impact
Coordinate assigned project workstreams - covering productivity, qualification, training, crowd communications, and QA - to ensure on-time delivery against execution standards
Deliver high-quality annotation and QA during POC and pilot phases to establish quality benchmarks and de-risk new client relationships from day one
Partner with Client Partners and Pilot Teams to validate quote assumptions, including time-per-task and quality requirements, before finalization
Analyze datasets to surface patterns, insights, and optimization opportunities that inform automation strategy and drive thought leadership content for customers
Support Project Managers and Client Partners as the AI expert point of contact across client meetings, workshops, QBRs, and status updates
Maintain version control and change communication protocols to ensure contributors are informed of guideline updates prior to launch and throughout production
Translate QA findings into targeted improvements to supporting materials, reducing recurring annotation errors and improving overall data quality
Provide data-backed findings and consultancy insight to strengthen Appen's advisory value during critical early project phases and ongoing client relationships
What You Bring
Proven experience coordinating or delivering data annotation, content evaluation, or AI training data projects in a structured delivery environment
Strong understanding of data quality principles, QA methodologies, and annotation workflows within GenAI or adjacent AI domains
Ability to create clear, accurate contributor-facing documentation including guidelines, FAQs, and cheatsheets under tight timelines
Experience supporting pilot or POC phases, including validating operational assumptions and establishing quality benchmarks
Demonstrated ability to analyse datasets, identify patterns, and translate findings into actionable insights or process improvements
Confident communicator with experience engaging cross-functional stakeholders, including client-facing participation in meetings, workshops, or reporting cycles
Disciplined approach to version control, update management, and change communication across distributed contributor teams
Comfortable operating across multiple concurrent workstreams with a high degree of ownership and attention to delivery standards
