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# Forward Deployed Engineer

- **Company:** Appen
- **Location:** United States (Remote available)
- **Salary:** $150,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Systems, Machine Learning, Delivery Pipeline, Model Validation, Generative AI, Data Pipelines
- **Published:** September 14, 2026
- **Apply:** https://www.builtincolorado.com/job/forward-deployed-engineer/11154600?handler=ApplyRedirect

## About the Role

* 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.

## Description

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.


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