> Markdown version of [/jobs/ext/4283890-senior-software-engineer-compute-infrastructure](https://www.wearedevelopers.com/jobs/ext/4283890-senior-software-engineer-compute-infrastructure). 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). --- # Senior Software Engineer, Compute Infrastructure - **Company:** Databricks - **Location:** Mountain View, CA, United States - **Experience:** Expert - **Salary:** $205,200.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Amazon Web Services, Microsoft Azure, C++ (Programming Language), Distributed Systems, Python (Programming Language), Scala (Programming Language), Google Cloud, System Availability, Multi-Cloud, Build Management, Kubernetes, Low Latency, Api Gateway, Serverless Computing, Docker, Databricks - **Published:** October 31, 2026 - **Apply:** https://www.adzuna.com/details/5885659910 ## About the Role * 5+ years of production-level experience in one of: Python, Java, Scala, C++, or a similar language * 4+ years of experience developing large-scale distributed systems from scratch * Experience with Kubernetes, Docker, or MicroVM technologies (e.g., Firecracker) and a strong grasp of cloud provider internals (AWS/Azure/GCP). * You aren't just a user of tools; you understand how to build them from the ground up when existing solutions fail at our scale. ## Description As an engineer on this team, you won't just manage infrastructure; you will build it as a product. You will tackle the hard problems of distributed systems: sub-second container/MicroVM startup times, multi-tenant security isolation, and building a unified orchestration layer that scales across AWS, Azure, and GCP. What You'll Do * Design and build the next generation of our Serverless platform, focusing on high availability, low latency, and scale-to-zero efficiency. * Build a single compute stack that supports heterogeneous workloads, including GPU instance qualification for AI training and stateful execution for Lakebase. * Solve Multi-Cloud Complexity: Develop platform-level abstractions that allow Databricks to run seamlessly across all major cloud providers. * Optimize everything from the API gateway down to the host-level networking and container orchestration. * Work alongside industry experts who have built world-class systems like Borg, Kubernetes, and Uber's infrastructure.