Head of Forward Deployed Engineering

Snorkel AI Snorkel AI · Data AI · Redwood City, CA +1 · 310 - DaaS FDE

Lead a forward-deployed engineering team focused on building and delivering high-quality datasets for AI initiatives. This involves defining data quality, implementing workflows, enhancing human-in-the-loop techniques, and owning systems for scalable data delivery. The role sits at the intersection of data engineering, ML engineering, and customer engagement, requiring strong leadership and hands-on technical skills in LLM-based workflows.

What you'd actually do

  1. Build and lead the Forward Deployed Engineering DaaS organization, setting a clear vision, defining the operating model and scaling its impact across Snorkel’s Expert Data-as-a-Service workflows
  2. Build, mentor, and motivate high performing teams, including cultivating skills and culture needed to consistently deliver exceptional outcomes and transformative impact.
  3. Own and evolve the data pipeline components of the DaaS stack, including model-assisted labeling and data generation, quality estimation, and data-centric feedback loops that guide human input
  4. Partner with customers - including research and engineering teams at Frontier AI Labs - to scope requirements for complex, novel AI datasets and translate needs into delivery-ready workflows
  5. Develop robust systems for request intake, task orchestration, SLA tracking, and progress monitoring to ensure seamless execution and prevent critical delivery gaps

Skills

Required

  • Applied data or ML engineering
  • Leading technical teams
  • Customer facing roles
  • Data pipelines
  • LLM-based workflows
  • Python
  • SQL
  • Data tooling (e.g., pandas, Plotly, Streamlit, Dash)

Nice to have

  • data annotation workflows
  • internal tooling for data delivery orgs

What the JD emphasized

  • build and lead our forward-deployed engineering (FDE) team
  • own quality in the end-to-end data pipeline
  • design innovative ML approaches
  • own systems and tools that enable consistent, scalable, and high-quality data delivery
  • founding member
  • 10+ years of experience in applied data or ML engineering roles, including 5+ years leading high-performing technical teams in hands-on management capacity
  • Proven track record of managing technical field teams in fast-paced, delivery-focused environments with competing priorities
  • Experience as a player-coach—comfortable being hands-on while supporting and scaling the team
  • Strong practical experience with LLM-based workflows

Other signals

  • data-centric AI
  • transform expert knowledge into specialized AI
  • build custom AI with their data
  • end-to-end data pipeline
  • enhance human-in-the-loop (HITL) techniques
  • data generation and review processes
  • scalable, and high-quality data delivery
  • data engineering, ML engineering, operations
  • ML-based workflows to improve data pipelines
  • data annotation workflows
  • training and evaluation data
  • LLM-based workflows