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Staff Software Engineer, Compute Infrastructure at Databricks - ApplyBlast

Staff Software Engineer, Compute Infrastructure

Databricks

4 months ago

4.1 on Glassdoor

Bellevue, Washington

Onsite

Full Time

Senior

0 applicants

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About this role

As a Staff Software Engineer and Tech Lead on Databricks' Compute Infrastructure team, you will design and build the systems powering the company's compute infrastructure to enable global data and AI workloads. This role involves developing compute abstractions, orchestrating workload scheduling, and scaling fleet management systems across millions of virtual machines on major cloud platforms. You will lead cross-team initiatives to raise the operational bar through strong architectural design and engineering excellence.

Responsibilities

Design and build compute abstractions for Databricks workloads
Develop workload orchestration and scheduling systems
Scale fleet management systems for millions of VMs daily
Lead cross-team initiatives across product and infrastructure
Raise technical standards through strong design practices and testing

Skills

Qualifications

BS in Computer Science10+ years of experience designing large-scale distributed systemsProficiency in Java, Scala, Go, or C++Experience with Kubernetes and DockerFamiliarity with AWS, Azure, or GCP

About Databricks

databricks.com

Headquarters

San Francisco, California, United States

Company Size

5001-10000 employees

Founded

2013

View Profile

Databricks is a unified Data Intelligence Platform that helps organizations build, deploy, and manage data, analytics, and AI at scale. Built on Apache Spark and Delta Lake, it provides a “lakehouse” architecture that combines data engineering, ETL, data warehousing, streaming, and machine learning (including MLflow and feature store) in a single managed service. The platform includes collaborative notebooks, scalable compute across major clouds, and built‑in governance, security, and performance optimizations. Enterprises use Databricks to simplify data pipelines, accelerate model development with a data‑centric approach, and operationalize analytics and AI.