Senior/Staff FDE - Synthetic Data Generation

About the job

Responsibilities

• Design and build scalable pipelines for synthetic data generation, transformation, filtering, and evaluation 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 create high-quality training and evaluation datasets covering 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 conduct experiments to measure the impact of synthetic data on downstream model performance and iteratively refine generation approaches.
• Package and deliver production-grade datasets in standardized formats with quality assurance and clear documentation.
• Lead technical workstreams from initial solution design through production delivery, managing ambiguity and making sound technical decisions.
• Build, refine, and iterate on solutions that meet customer needs, incorporating feedback to ensure tangible value.
• Rapidly prototype and productionize solutions involving models, data pipelines, APIs, and custom applications.
• Clearly communicate technical tradeoffs, experimental results, and recommendations to technical and cross-functional stakeholders.
• Act as a trusted technical partner to customers and internal delivery teams, resolving complex blockers and driving alignment.
• Identify recurring patterns across customer engagements and develop reusable pipelines, evaluators, tooling, and best practices.
• Define and improve technical standards for synthetic data generation, experimentation, evaluation, and delivery.
• Collaborate 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 mentor other engineers.
• Stay current with emerging synthetic data, LLM evaluation, and data curation techniques and assess their applicability to customer problems.

Requirements

• 5+ years of experience in machine learning engineering, data science, applied AI, forward deployed engineering, or a similar technical role.
• Strong Python skills and experience building reliable production data or ML systems.
• Experience with containerization using Docker and deploying on cloud platforms (e.g., AWS, GCP, or Azure).
• Hands-on experience with LLMs, including building model-based applications and data workflows with the modern GenAI/LLM stack.
• Experience integrating systems, models, and data sources through APIs.
• Strong understanding of ML experimentation and evaluation, including defining metrics and using empirical results to guide technical decisions.
• Experience building synthetic data, data augmentation, or model-generated training and evaluation datasets.
• Experience with LLM evaluation techniques such as LLM-as-a-judge, model-based evaluation, rubric-based evaluation, or custom evaluators.
• Demonstrated ability to manage ambiguous technical problems from definition to delivery.
• Strong technical communication skills and experience working directly with customers and cross-functional stakeholders.
• Experience serving as a technical lead, setting technical direction, driving architecture and key decisions, mentoring engineers, and creating reusable approaches.