> Markdown version of [/jobs/ext/3627588-staff-backend-software-engineer-databricks-ai](https://www.wearedevelopers.com/jobs/ext/3627588-staff-backend-software-engineer-databricks-ai). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Backend Software Engineer- (Databricks AI) - **Company:** Databricks - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Salary:** $166,000.0 - $225,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Computer Programming, Distributed Systems, Monitoring of Systems, Python (Programming Language), Azure Machine Learning, Service-Oriented Architecture, Cloud Platform System, Pytorch, Delivery Pipeline, Large Language Models, Backend, Low Latency, Machine Learning Operations, Restful APIs, Automation Anywhere, Databricks - **Published:** October 8, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=15224c87c6ce3cd3 ## About the Role * 5+ years of experience in backend or infrastructure engineering * Strong programming skills in Scala, Go, or Python * Experience with distributed systems, scalable APIs, or cloud-native infrastructure * Familiarity with service-oriented architecture, deployment pipelines, and system observability * Strong product and ownership mindset - you care about building the right solution, not just any solution, * Experience with real-time serving, ML infrastructure, or GPU orchestration * Exposure to platforms like SageMaker, Vertex AI, or Azure ML * Contributions to OSS projects like MLflow, PyTorch, or Ray * Built developer platforms or internal tools supporting AI workflows Pay Range Transparency ## Description * Build infrastructure that powers our flagship offerings like MLflow, AI Gateway, Databricks Apps, Agent Framework, Agent Bricks, and Foundation Model APIs, to state a few. * Improve reliability, latency, and efficiency of distributed AI workloads * Collaborate with platform, infra, and ML teams to deliver seamless end-to-end experiences * Shape how developers and data scientists build and interact with AI on Databricks