Staff Software Engineer - AI Research Infrastructure
Databricks · New York City, NY | San Francisco, CA · Executive Engineering - Pipeline
About this role
Databricks is hiring a staff-level Infrastructure Engineer in the software engineering function based in New York City, NY | San Francisco, CA. The posting calls out experience with Java, Rust, Scala, Kubernetes. Compensation is listed at $190,000–$270,000 per year.
- Role
- Infrastructure Engineer
- Function
- software engineering
- Level
- staff
- Track
- Tech leadership
- Employment
- Full-time
- Location
- New York City, NY | San Francisco, CA
- Department
- Executive Engineering - Pipeline
More roles at Databricks
Job description
from Databricks careersStaff Software Engineer - AI Research Infrastructure
P-1215
At Databricks, we are obsessed with enabling data teams to solve the world’s toughest problems, from security threat detection to cancer drug development. We do this by building and running the world’s best data and AI platform so our customers can focus on the high-value challenges that are central to their own missions.
The Databricks AI Research organization enables companies to develop AI models and agents using their own data, with technologies ranging from post-training open source LLMs to developing advanced multi-agent architectures. Databricks AI does so by producing novel science and putting it into production. Databricks AI is committed to the belief that a company’s AI models and agents are just as valuable as any other core IP, and that high-quality AI should be available to all.
Job Description
As a Staff Software Engineer, AI Research Infrastructure, you will be developing and running the research stack that powers Databricks AI Research. You will design and build services that schedule, orchestrate, and observe large‑scale training and inference experiment workloads across thousands of GPUs, improve our dev tooling and ensure that researchers can iterate quickly without sacrificing reliability, efficiency, or security.
You’ll partner closely with research scientists, ML engineers, and platform teams to turn experimental workloads into robust, repeatable pipelines, and to push the limits of what our infrastructure can support.