Research Engineer – Training Infra
Snorkel AI · Redwood City, CA (Hybrid) | San Francisco, CA (Hybrid) | Remote (United States) · 316 - Research
About this role
Snorkel AI is hiring a mid-level Research Scientist in the machine learning function as a remote position. The posting calls out experience with Python, AWS, Kubernetes, LLMs. Compensation is listed at $180,000–$250,000 per year.
- Role
- Research Scientist
- Function
- machine learning
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- Redwood City, CA (Hybrid) | San Francisco, CA (Hybrid) | Remote (United States)
- Work mode
- Remote
- Department
- 316 - Research
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!
THE ROLE
As an Applied Research Engineer at Snorkel AI, you will own the infrastructure that powers our model training and evaluation work. This is a hands-on role where you will build and operate GPU cluster infrastructure, training pipelines, and the tooling that allows our research and engineering teams to run experiments reliably and at scale. You will work closely with research scientists and engineers, translating training requirements into robust, reproducible systems—and proactively removing infrastructure blockers before they slow down the work that matters most.