Senior/Staff FDE - Synthetic Data Generation (AI)

Location: Hybrid in New York City, NY, or San Francisco, CA

Salary: $180,000–$320,000 USD base salary per year, plus variable compensation, equity, and benefits.

Company

Snorkel AI helps enterprises transform expert knowledge and data into specialized, production-ready AI systems.

What you will do

• 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 for 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 customer requirements.
• Lead customer engagements from technical discovery and solution design through implementation, evaluation, and production delivery.
• Turn recurring customer solutions into reusable pipelines, tooling, technical standards, and product improvements while guiding other engineers.

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, containerizing with Docker, and deploying on AWS, GCP, or Azure.
• Hands-on experience building LLM-based applications and data workflows and integrating models, systems, and data sources through APIs.
• Strong understanding of ML experimentation and evaluation, including metrics definition and empirical decision-making.
• Experience building synthetic data, data augmentation, or model-generated training and evaluation datasets, including LLM-as-a-judge or custom evaluation techniques.
• Experience taking ambiguous technical problems through delivery, communicating with customers and cross-functional stakeholders, and providing technical leadership, mentoring, and architectural direction.

Nice to have

• Experience developing fine-tuning, preference optimization, or benchmarking datasets with human-in-the-loop workflows.
• Experience building agentic environments and tasks, including repo-scale coding tasks, tool-agent-user interactions, and agent tool protocols.
• Experience with reinforcement learning for LLMs, reward and verifier design, or RL with verifiable rewards.
• Experience in fast-paced, customer-facing environments with evolving requirements and technical approaches.

Culture & Benefits

• Work in a rapidly scaling company with market-proven solutions and robust funding.
• Opportunities to shape priorities, influence technical and strategic decisions, and impact product direction.
• Support for deepening technical expertise, developing leadership skills, and learning across functions.
• Employee stock options and benefits are included with all offers.
• Equal employment opportunities and reasonable accommodations are provided.