Manager, Forward Deployed Engineering
$200k - $320k • Remote • New York City, NY (Hybrid); San Francisco, CA (Hybrid)
Posted 25d ago
Job Location
New York City, NY (Hybrid); San Francisco, CA (Hybrid)
Tech Stack
Remote Work Policy
Fully remote
Categories
AI Engineer
About the job
Snorkel AI is seeking a Manager, Forward Deployed Engineering to establish and lead the Forward Deployed Engineering (FDE) function within the Data-as-a-Service (DaaS) organization. This role is responsible for the technical execution of DaaS delivery, managing the systems, workflows, and quality frameworks for large-scale dataset production. As a player-coach, you will define strategy and architecture while remaining hands-on with implementation, working at the intersection of data engineering, ML engineering, and production operations to enhance technical delivery capabilities.
Responsibilities
- Build and lead the Forward Deployed Engineering function, defining its operating model, technical roadmap, and execution standards.
- Hire, mentor, and develop a high-performing engineering team.
- Own the design and evolution of end-to-end AI data pipelines, including dataset generation, evaluation systems, and quality frameworks.
- Drive the development of ML-assisted and human-in-the-loop (HITL) workflows to improve data production speed, scalability, and reliability.
- Establish and standardize measurement, benchmarking, and validation systems for consistent, high-quality dataset delivery.
- Develop scalable internal tooling and workflows for orchestration, monitoring, and delivery.
- Partner cross-functionally with Delivery, Engineering, and Research to productionize new approaches and improve DaaS capabilities.
Requirements
- 6+ years of experience in AI/ML/GenAI engineering, forward deployed engineering, solution engineering, or applied AI roles with strong 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.
- Experience designing evaluation frameworks, quality measurement systems, or benchmarking approaches for ML systems.
- Strong systems thinking ability to design scalable, reusable solutions.
- Proven ability to operate in ambiguous, fast-paced environments and drive cross-functional execution.
- Start-up or growth-stage experience is strongly preferred.
- Customer-facing, consulting, or services experience is strongly preferred.
- Experience with human-in-the-loop (HITL) systems, data annotation workflows, or data-centric AI approaches is a plus.
- Familiarity with synthetic data generation or model-assisted labeling techniques is a plus.
- Experience building internal platforms or tooling for delivery or operations teams is a plus.
Benefits
- Equity
- Benefits