Senior/Staff FDE - Synthetic Data Generation

Snorkel AI helps enterprises transform expert knowledge into specialized AI at scale through high-quality data. The Forward Deployed Engineer will lead customer engagements focused on synthetic data generation, building scalable generation and evaluation pipelines, improving model outcomes, and turning successful solutions into reusable capabilities and product improvements.

Responsibilities:

• Design and build scalable synthetic data generation, transformation, filtering, and evaluation pipelines for complex AI use cases
• Translate model objectives, failure modes, and data gaps into synthetic data strategies, experiments, and technical specifications
• Develop LLM- and ML-assisted workflows to generate high-quality training and evaluation datasets across targeted behaviors, domains, and edge cases
• Build automated evaluators, quality checks, and measurement frameworks to assess correctness, relevance, diversity, coverage, and adherence to customer requirements
• Design and run experiments to measure the impact of synthetic data on downstream model performance and iteratively improve generation approaches
• Package and deliver production-grade datasets with standardized formats, quality assurance, and clear documentation
• Lead technical workstreams from initial solution design through production delivery, navigating ambiguity and making sound technical decisions
• Build, refine, and iterate on solutions that address customer needs, incorporating feedback to ensure the delivered work provides tangible value
• Rapidly prototype and productionize solutions across models, data pipelines, APIs, and custom applications
• Communicate technical tradeoffs, experimental results, and recommendations clearly to technical and cross-functional stakeholders
• Serve as a trusted technical partner to customers and internal delivery teams, resolving complex blockers and driving alignment
• Identify recurring patterns across customer engagements and turn successful solutions into reusable pipelines, evaluators, tooling, and best practices
• Define and improve technical standards for synthetic data generation, experimentation, evaluation, and delivery
• Partner with DaaS Engineering and Product teams to influence platform and product capabilities based on real-world customer needs
• Lead technical design reviews, share expertise, and provide guidance to other engineers
• Stay current with emerging synthetic data, LLM evaluation, and data curation techniques and assess their applicability to customer problems