VP, Head of Forward Deployed Engineering

In this role, you will lead and scale Snorkel’s entire Forward Deployed Engineering (FDE) organization. These are the engineers who design, build, and run the data pipelines used by the world’s leading AI labs and enterprises. You’ll shape the function’s direction, drive its execution, and manage our fastest-growing team as it scales from 40 to 100+ engineers.

You’ll set the technical and organizational strategy that turn one-off implementations into reusable solutions (pipelines, evals, augmentations, environments) and invest in shared ways of working. In our product lifecycle FDE sits between Research (defines novel approaches and needs for training) and Engineering (scales our platform and pipelines from 1-100). FDE owns the most important middle stretch, taking every new investment from 0-1.

Your organization is responsible for both what we deliver and how we deliver it. Just as engineering is the final gate for what goes to production, FDE is the final gate for every dataset that ships to a customer. Nothing goes out without your team’s sign-off. You’ll also own the pipelines that produce that data, from cost and throughput to latency and efficiency.

This role is for a deeply technical leader who is, first and foremost, a doer. You’ll understand pipeline architecture enough to challenge designs and propose direction, while coaching your managers and tech leads to think beyond their individual projects. In a fast-moving market where resourcing shifts often, you’ll be the steady hand that keeps teams focused on what customers actually need, not just what was specified., * Lead and scale the FDE organization: set the vision, define the operating model and org design, and align team plans and strategy with company priorities.

• Drive hiring in hypergrowth: build a strong bench of engineering managers and tech leads, and keep the hiring bar high while the organization scales rapidly. You’ll grow to 100+ engineers in the coming quarters.
• Coach and develop EMs and TLMs who run pods of FDEs, helping them grow from single-project owners into leaders who see and solve problems across the delivery org.
• Drive reuse across the organization: identify patterns across bespoke deliveries, have FDE build the first version, and partner with platform engineering to scale them company-wide.
• Set engineering standards for how the FDE organization works including pipeline design, data evaluation and verification, delivery sign-off and root cause analysis, so every team operates to one standard.
• Establish FDE as the final point of control for delivery quality: define quality bars, own sign-off on every dataset that ships, and make sure every customer requirement is verified
• Own pipeline cost and performance, including token and compute cost, throughput, latency, and be responsible for targets and forecasts your teams deliver against.
• Stay deep in the architecture of the organization’s pipelines: review designs, find weaknesses, and propose changes that improve quality, cost, and reuse.
• Plan and adjust capacity as priorities shift, rebalancing teams quickly while keeping delivery commitments intact.

• 10+ years in software or ML engineering, including 5+ years leading multiple teams as a manager of managers.
• Experience scaling an engineering organization of ~100 engineers, owning team planning, strategy, and alignment with company needs.
• A strong engineering background in software or ML engineering. A background in ML infrastructure or data platforms is strongly preferred.
• Has built and shipped AI/ML systems to production with strong experience using automated verification to decide what’s good enough to ship.
• A track record of hiring and scaling engineering teams through hyper growth.
• Deep technical fluency: can go deep in architecture reviews and propose meaningful design changes, not just manage from a distance. Direct experience with LLM application stacks (agents, orchestration, LLM-based pipelines).
• Strong customer accountability: delivers on what customers intend, not just the literal spec, and adapts as their needs change.

Actual compensation will be determined based on factors including skills, qualifications, experience, and geographic location.