Senior/Staff FDE - Synthetic Data Generation

About Snorkel

Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes.

Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!

About the Role

Snorkel AI is hiring a Forward Deployed Engineer focused on Synthetic Data Generation to partner with leading AI labs and enterprises on their most critical AI initiatives.

In this role, you will lead the technical execution of complex customer engagements where synthetic data is used to improve model training, evaluation, and performance. You will translate ambiguous model and data challenges into effective data strategies, build scalable generation and evaluation pipelines, and use experimentation to continuously improve data quality and downstream model outcomes.

You will work across the full delivery lifecycle—from technical discovery and solution design through implementation, evaluation, and production delivery. You will also identify patterns across engagements and turn successful approaches into reusable capabilities, technical standards, and product improvements.

• Main Responsibilities
• Synthetic Data Generation & Evaluation
• 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
• Forward Deployed Engineering & Customer Partnership
• 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
• Technical Leadership & Scale
• 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

What We're Looking For

• 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, including containerizing with Docker and deploying on cloud platforms (e.g., AWS, GCP, or Azure)
• Hands-on experience with LLMs—building model-based applications and data workflows with the modern GenAI/LLM stack, and 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, including LLM-as-a-judge, model-based evaluation, rubric-based evaluation, or custom evaluators
• Demonstrated ability to take ambiguous technical problems from problem definition through delivery, with strong technical communication 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 that influence broader engineering or product outcomes

Preferred Qualifications

• Experience developing datasets for fine-tuning, preference optimization, or benchmarking, including human-in-the-loop generation and review workflows
• Experience building agentic environments and tasks—repo-scale coding tasks, tool-agent-user interaction design, and agent tool protocols and interoperability
• Experience with reinforcement learning for LLMs, including reward and verifier design and RL with verifiable rewards (RLVR)
• Experience working in fast-paced, customer-facing environments where requirements and technical approaches evolve quickly