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Rubrik Job Board

Software Engineer, Atlas Distributed Systems

Palo Alto, CAFrom $237kmidAdded today

About this role

Rubrik seeks an experienced distributed systems engineer to architect and advance Atlas, its core distributed filesystem powering data protection across data centers, edges, and cloud environments. You'll design scalable storage solutions, guide multi-team technical direction, and tackle challenges spanning cloud cost optimization, deduplication, performance, and data security.

What you'll do

  • Design and develop distributed filesystem features including cloud-backed storage, deduplication, compression, and snapshotting
  • Establish architectural principles and prototypes for Atlas platform evolution across multiple Rubrik products
  • Lead technical direction impacting teams building on Atlas, including Kubernetes, Oracle, and cloud application protection
  • Optimize read/write performance, reliability, resiliency, and infinite scalability using techniques like eBPF tooling and read-ahead strategies
  • Address data integrity, security, and immutability requirements for cloud environments
  • Collaborate with cross-functional teams to realize production-quality solutions

What they're looking for

  • Distributed systems design and development
  • Distributed filesystem architecture and implementation
  • C++ programming
  • Backend systems at scale
  • Cloud platforms (AWS, Azure, GCP) and cost analysis
  • Storage systems and data protection
  • Performance optimization and eBPF
  • Asynchronous programming
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Rubrik Job Board

Rubrik builds Disaster Recovery as a Service solutions across cloud platforms with a focus on innovative backend systems and security. The company is hiring Software Engineers, Sales Engineers, and Application Security Engineers to expand its engineering, sales, and security capabilities.

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Likely interview questions

  • Describe your experience designing and implementing distributed filesystems. What were the key architectural decisions you made?
  • How have you optimized performance in large-scale backend systems, and what metrics did you use to measure success?
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