About the job
P-940
(Position Location is open to both our Seattle & Bellevue offices.)
At Databricks, we are passionate about empowering data teams to tackle some of the world's most challenging problems, from detecting security threats to developing life-saving cancer treatments. We achieve this by creating and managing the leading data and AI infrastructure platform, allowing our customers to concentrate on the critical challenges central to their missions.
Founded in 2013 by the original creators of Apache Spark, Databricks has rapidly transformed from a small office in Berkeley, California, into a global entity with over 1,000 employees. We are trusted by thousands of organizations, ranging from startups to Fortune 100 companies, with their mission-critical workloads, establishing us as one of the fastest-growing SaaS companies worldwide.
Our engineering teams are dedicated to building highly technical products that address significant real-world needs. We continuously push the boundaries of data and AI technology while maintaining the resilience, security, and scalability necessary for our customers' success on our platform.
We operate one of the largest software platforms, managing millions of virtual machines, generating terabytes of logs, and processing exabytes of data daily. At this scale, we frequently encounter faults in cloud hardware, networks, and operating systems, and our software must effectively shield our customers from such issues.
As a Backend Software Engineer, you will collaborate closely with your team and product management to prioritize, design, implement, test, and maintain microservices for the Databricks platform and products. Responsibilities include writing software in Scala/Java, building data pipelines (Apache Spark, Apache Kafka), integrating with third-party applications, and interacting with cloud APIs (AWS, Azure, CloudFormation, Terraform).
Potential teams you may join include:
Data Science and Machine Learning Infrastructure: Develop services and infrastructure at the intersection of machine learning and distributed systems, empowering our flagship collaborative workspace, notebooks, IDE integrations, and project management tools. Our technology also facilitates machine learning at scale through tools for environment management, distributed training, and managing the ML lifecycle via MLflow.
Compute Fabric: Create the resource management infrastructure that supports all big data and machine learning workloads on the Databricks platform, ensuring robustness, flexibility, and security.
