Role Purpose
The Forward Deployed Engineer works directly with enterprise clients to design, deploy, and optimise GenAI data solutions, with full ownership of final delivered data quality. Embedded across engineering, data operations, and client delivery, this role ensures datasets, pipelines, workflows, and automations meet defined accuracy, reliability, and performance requirements for GenAI model development.
Your Impact
Act as a trusted consultative partner to clients by defining requirements, clarifying success criteria, shaping high‑quality annotation guidelines, and proactively providing insights and innovative solutions that strengthen trust and improve data quality, efficiency, and model outcomes.
Influence the what and the why behind GenAI data solutions by translating ambiguous client needs into clear, scalable workflows.
Deliver zero-defect GenAI datasets that meet all defined acceptance and quality criteria.
Build scalable, reproducible, and observable validation and automation pipelines that reduce variance, manual review effort, rework, and time to validation.
Contribute reusable quality frameworks and validators adopted across multiple projects.
What you Bring
Experience working on GenAI, machine learning, or model evaluation data.
A consultative problem-solving approach when leading client conversations, asking sharp questions, and guiding stakeholders toward better decisions.
Hands-on experience designing and implementing data pipelines, validation systems, or workflow automation.
Previous experience in owning final data quality and acting as the authority on delivery readiness.
The ability to translate business or model development needs technical workflows and validation logic.
Confidence in working directly with client engineering or machine learning teams.
Strong documentation that prioritizes reproducibility, observability, and reliability.
Compensation
Additional Information
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 total target compensation for this full-time role ranges from $150K–$200K USD annually, inclusive of base salary and performance-based incentive. Actual compensation is determined based on role, level, experience and technical expertise. Appen offers a comprehensive benefits package, including medical, dental, and vision coverage, retirement plan options and paid time off.
Appen is a leader in AI training data with over 30 years of experience specializing in human-generated data to train, fine-tune, and evaluate models across generative AI, large language models, computer vision, and speech recognition. The company operates an AI-assisted data annotation platform supported by a global crowd of more than 1 million contributors across over 200 countries. Appen's services support model pre-training, supervised fine-tuning, evaluation and benchmarking, safety and red teaming, and multilingual global expansion. The company partners with frontier AI labs, Big Tech companies, and enterprise AI adopters to deliver the high-quality data that enables AI models to perform effectively.
