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.
Why You’ll Love Working Here
At Appen, we foster a culture of innovation, collaboration, and excellence. We value curiosity, accountability, and a commitment to delivering the highest quality AI solutions for frontier models. You'll work on complex challenges that shape the future of AI across industries and geographies, alongside talented people in a culture that values humility over ego. You'll have the flexibility to deliver in a way that works for you and your team, supported by tools, resources, and development opportunities to continue to build your capability over time.
About Appen
Appen has been a leader in AI training data for over 30 years. We specialise in human generated data to train, fine tune, and evaluate models across generative AI, large language models, computer vision, and speech recognition. Our AI assisted data annotation platform and global crowd of more than 1 million contributors in over 200 countries support model pre training, supervised fine tuning, evaluation and benchmarking, safety and red teaming, and multilingual global expansion.
The hourly target compensation for this full-time role ranges from $20 –$25 USD per hour. Actual compensation is determined based on role, level, location, experience and technical expertise. Appen offers a comprehensive benefits package, including medical, dental, and vision coverage, retirement plan options and paid time off.
- 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
- 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