Manager, Forward Deployed Engineering
Snorkel AI · New York City, NY (Hybrid) | Redwood City, CA (Hybrid) | San Francisco, CA (Hybrid) · 310 - DaaS FDE
About this role
Snorkel AI is hiring a manager-level Engineering Manager in the software engineering function based in New York City, NY (Hybrid) | Redwood City, CA (Hybrid) | San Francisco, CA (Hybrid) (hybrid). The posting calls out experience with Python, SQL, LLMs, API Development. Compensation is listed at $220,000–$320,000 per year.
- Role
- Engineering Manager
- Function
- software engineering
- Level
- manager
- Track
- hybrid
- Employment
- Full-time
- Location
- New York City, NY (Hybrid) | Redwood City, CA (Hybrid) | San Francisco, CA (Hybrid)
- Work mode
- Hybrid
- Department
- 310 - DaaS FDE
More roles at Snorkel AI
Job description
from Snorkel AI careersAbout 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 at scale. The AI landscape has gone through incredible changes between 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!
About the Role
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.