Senior/Staff Forward Deployed Engineer - Data as a Service
Builds and deploys ML-driven data pipelines and evaluation workflows for enterprise AI projects, working with Python, SQL, and LLM-based systems to deliver production-grade datasets and tools.
- Compensation
- $150K–$320K
- Location
- New York City, NY (Hybrid); San Francisco, CA (Hybrid)
- Work setup
- Hybrid
- Seniority
- Staff
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Solutions engineering
- Posted
Skills
Must have
- 5+ years of experience in data science, data engineering, ML engineering, solutions engineering, forward-deployed engineering, or other technical solution development roles.
- Strong practical experience with Python and SQL data tooling required.
- Familiarity with ML and LLM-based solutions, including applying ML techniques in production contexts and building validation, evaluation, or quality measurement workflows for ML/LLM-based systems.
- Experience translating ambiguous customer or stakeholder requirements into technical specifications, workflows, and delivered solutions.
- Experience integrating systems via APIs and building custom tooling or workflows for non-standard technical requirements.
Full description
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 - Multiple Levels
This is an engineering delivery role where you’ll translate ambiguous customer needs into production-grade data, evaluation, and ML-assisted workflows. This is a high-impact role focused on end-to-end ownership of the AI data pipeline lifecycle, including developing and deploying ML-based workflows and building the technical foundations that make our human-in-the-loop (HITL) data generation and review faster, more reliable, and more effective.
You’ll work at the critical intersection of data science, data engineering, AI engineering and operations, partnering closely with our DaaS Delivery Operations team and cross-functional stakeholders. You’ll develop technical specifications, design evaluation workflows, implement quality standards, measurement frameworks, and ML-assisted applications which improve our data pipelines and unblock projects through technical innovation.
This role is ideal for someone who is comfortable working throughout the delivery lifecycle, rolling up their sleeves to solve complex multi-faceted problems, thrives as a technical communicator and works well as a key member of a team.
Main Responsibilities
Project Execution & Delivery
- Build and deploy evaluators, design and implement quality measurement systems to validate project outputs and ensure deliverables meet client expectations
- Generate synthetic datasets by developing or adapting existing pipelines to accelerate client engagements and augment training data
- Package and deliver production-grade datasets with standardized formatting, comprehensive documentation, and quality assurance
- Configure and build custom applications and off-platform solutions for non-standard or specialized client requirements
Production & Technical Partnership
- Define production specifications and workflows, securing technical alignment with client teams to enable seamless go-live transitions
- Provide ongoing technical support to Delivery Managers, addressing complex questions, resolving technical blockers, and supporting customer rebuttals
- Maintain specification consistency and alignment across customer and internal teams throughout the engagement lifecycle