Manager, Forward Deployed Engineering

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

Manager for Forward Deployed Engineering (FDE) within the Data-as-a-Service (DaaS) organization. This role leads a team responsible for the technical execution of DaaS delivery, owning systems, workflows, and quality frameworks for high-quality dataset production at scale. Responsibilities include building and leading the FDE function, defining its operating model and roadmap, hiring and mentoring engineers, owning AI data pipelines (generation, evaluation, quality), driving ML-assisted and HITL workflows, establishing measurement and benchmarking systems, and developing internal tooling. The role requires strong hands-on technical depth in AI/ML/GenAI engineering, experience leading engineers, building/operating production ML/LLM workflows, proficiency in Python/SQL, and experience with evaluation frameworks.

What you'd actually do

  1. Build and lead the Forward Deployed Engineering function, defining the team’s operating model, technical roadmap, and standards for execution
  2. Hire, mentor, and develop a high-performing team, fostering strong ownership, velocity, and engineering excellence
  3. Own the design and evolution of end-to-end AI data pipelines, including dataset generation, evaluation systems, and quality frameworks
  4. Drive development of ML-assisted and HITL workflows that improve the speed, scalability, and reliability of data production
  5. Establish and standardize measurement, benchmarking, and validation systems to ensure consistent, high-quality dataset delivery

Skills

Required

  • Python
  • SQL
  • modern data tooling
  • integrating systems via APIs
  • systems thinking
  • leading engineers
  • building and operating data pipelines
  • ML/LLM-based workflows in production
  • evaluation frameworks
  • quality measurement systems
  • benchmarking approaches for ML systems

Nice to have

  • human-in-the-loop (HITL) systems
  • data annotation workflows
  • data-centric AI approaches
  • synthetic data generation
  • model-assisted labeling techniques
  • building internal platforms or tooling

What the JD emphasized

  • hands-on technical depth
  • player-coach capacity
  • building and operating data pipelines and ML/LLM-based workflows in production environments
  • designing evaluation frameworks, quality measurement systems, or benchmarking approaches for ML systems
  • systems thinking
  • Customer-facing, consulting, or services experience is strongly preferred

Other signals

  • data-centric AI
  • dataset production at scale
  • human-in-the-loop data generation
  • ML-assisted workflows
  • evaluation systems