Senior/Staff FDE - Synthetic Data Generation

The role

You will lead technical execution for customer engagements using synthetic data to improve model training, evaluation, and performance. You will design scalable data pipelines and work across the full delivery lifecycle from discovery to production.

What you will do

• Design and build scalable synthetic data generation, transformation, filtering, and evaluation pipelines
• Translate model objectives and data gaps into synthetic data strategies and technical specifications
• Develop LLM- and ML-assisted workflows to generate training and evaluation datasets
• Build automated evaluators, quality checks, and measurement frameworks
• Design and run experiments to measure the impact of synthetic data on model performance
• Package and deliver production-grade datasets with documentation
• Lead technical workstreams from solution design through production delivery
• Prototype and productionize solutions across models, data pipelines, APIs, and custom applications
• Communicate technical tradeoffs and recommendations to stakeholders
• Identify recurring patterns to create reusable pipelines, tooling, and best practices

Required qualifications

• 5+ years of experience in machine learning engineering, data science, applied AI, forward deployed engineering, or a similar technical role
• Strong Python skills
• Experience building reliable production data or ML systems
• Experience containerizing with Docker
• Experience deploying on cloud platforms (e.g., AWS, GCP, or Azure)
• Hands-on experience with LLMs
• Experience 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
• 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 definition through delivery

Nice to have

• 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
• 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

Benefits

• Equity
• Benefits

About the company

Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises.