Job description

RDQ427R198 The Opportunity Multi-cloud is core to Databricks product strategy. Compute serves as the layer that abstracts the cloud from the rest of the company. Building unified infrastructure on top of heterogeneous clouds is a challenge that few companies experience. 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. What We’re Looking For 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. Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here . Local Pay Range $164,200 — $205,200 USD About Databricks Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn , X , YouTube , and Instagram . Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here . Our Commitment to Diversity and Inclusion At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics. Compliance If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.

Requirements

Must-haves

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.
What We’re Looking For
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.
Our Commitment to Diversity and Inclusion

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

restawsazuregcpaiapikubernetespythonjavadocker

Platform: greenhouse
Effort: quick