Forward Deployed Engineer - Data as a Service

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

Technical Leadership & Innovation

• 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

What We’re Looking For

• 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.

Why Join Snorkel AI?

At Snorkel AI, we're building the future of data-centric AI. Our Expert Data-as-a-Service organization partners with world-class customers to solve some of the hardest data challenges — creating training and evaluation data that power the next generation of LLMs and AI systems. You'll work directly on projects that impact real production systems, while shaping how internal teams deliver faster, better, and more intelligently. This is a rare opportunity to own technical data workflows and be a founding member of the technical DaaS team.

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Compensation

The base salary range for this position is $150,000 – $350,000. This range reflects multiple factors, including role level and work location. The final offer within this range will be determined based on job-related skills, experience, relevant education or training, interview performance, and other business considerations.

All offers also include equity in the form of employee stock options, as well as benefits.