Forward Deployed Engineer - Data Service

About Snorkel

At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data. We’re on a mission to help enterprises transform expert knowledge into specialized AI  AI landscape has gone through incredible changes since 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems.

We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before.

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.

• 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

• 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
• Identify and document workflow best practices and automation opportunities, collaborating with DaaS Engineering to continuously improve delivery capabilities

• Maintain solution leaderboards and execute custom model benchmarking on existing datasets to demonstrate technical capabilities
• Drive continuous improvement of technical assets, evaluation frameworks, and delivery processes to enhance speed, quality, and scalability

• 3+ 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.

The base salary range for this position is $150,000 – $320,000
. This range reflects…