Manager, Forward Deployed Engineering

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. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!

Snorkel AI is hiring a Manager, Forward Deployed Engineering to build and lead our Forward Deployed Engineering (FDE) function within the Data-as-a-Service (DaaS) organization.

This team is responsible for the technical execution layer of DaaS delivery — owning the systems, workflows, and quality frameworks that power high-quality dataset production at scale.

You will lead a team of engineers who operate across the full AI data lifecycle: designing evaluation systems, building ML‑assisted workflows, and developing the technical foundations that make human‑in‑the‑loop (HITL) data generation faster, more reliable, and scalable.

This is a hands‑on, player‑coach role where you’ll both help define the strategy and architecture of our data pipelines while staying close to implementation. As a founding leader, you will establish how FDE partners with Delivery, Engineering, and Research to ensure consistent, high‑quality execution across all DaaS projects.

You’ll operate at the intersection of data engineering, ML engineering, and production operations, driving the evolution of our technical delivery capabilities and enabling Snorkel to scale its impact across increasingly complex AI workloads.

• Build and lead the Forward Deployed Engineering function, defining the team’s operating model, technical roadmap, and standards for execution
• Hire, mentor, and develop a high‑performing team, fostering strong ownership, velocity, and engineering excellence
• Own the design and evolution of end‑to‑end AI data pipelines, including dataset generation, evaluation systems, and quality frameworks
• Drive development of ML‑assisted and HITL workflows that improve the speed, scalability, and reliability of data production
• Establish and standardize measurement, benchmarking, and validation systems to ensure consistent, high‑quality dataset delivery Develop scalable internal tooling and workflows for orchestration, monitoring, and delivery, reducing bottlenecks and improving throughput
• Partner cross‑functionally with Delivery, Engineering, and Research to product ionize new approaches and continuously improve DaaS capabilities

• 6+ years of experience in AI/ML/GenAI engineering, forward deployed engineering, solution engineering or applied AI roles, with strong hands‑on technical depth
• 2+ years of experience leading or managing engineers in a hands‑on, player‑coach capacity
• Strong experience building and operating data pipelines and ML/LLM‑based workflows in production environments
• Proficiency in Python, SQL, and modern data tooling; experience integrating systems via APIs. Has written code in the last 2 years
• Experience designing evaluation frameworks, quality measurement systems, or benchmarking approaches for ML systems
• Strong systems thinking — ability to design scalable, reusable solutions rather than one‑off implementations
• Proven ability to operate in ambiguous, fast‑paced environments and drive execution across cross‑functional teams. Start‑up or growth‑stage experience is strongly preferred.
• Customer‑facing, consulting, or services experience is strongly preferred

Nice to have:

• Experience with human‑in‑the‑loop (HITL) systems, data annotation workflows, or data‑centric AI approaches.
• Familiarity with synthetic data generation or model‑assisted labeling techniques.
• Experience building…