Senior Backend Engineer, Filesystem & Distributed Systems

Cacheflow

San Francisco (CA)

On-site

USD 166,000 - 225,000

Full time

4 days ago
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Job summary

Databricks is seeking a Senior Software Engineer for the Files team to push the boundaries of storage and distributed systems. You will shape the filesystem strategy to power agentic workloads and data-centric platforms, collaborating across platform teams.

You will design and optimize a filesystem powering tens of thousands of workloads, with responsibilities spanning API to kernel, and contributing to compute/storage architecture evolutions.

Qualifications

  • Bachelor's degree in Computer Science or related field
  • 5+ years production-level experience in Java, Scala, C++, Go, Rust, or similar language
  • 3+ years of experience developing large-scale distributed systems
  • Experience with Kubernetes, Docker, or MicroVM technologies (e.g., Firecracker) and cloud provider internals (AWS/Azure/GCP)
  • Experience with filesystems and FUSE
  • Experience building object/blob storage (S3, Azure Blob Storage, GCS)
  • Systems performance engineering across microservices, down to Linux

Responsibilities

  • Design, build, and operate the filesystem platform powering tens of thousands of data, AI and agentic workloads daily
  • Push filesystem performance for the most demanding workloads—profiling and optimizing across the stack, from the API layer down to the Linux kernel
  • Partner with teams across the platform to design and evolve next-generation compute and storage architectures
  • Set the technical direction for how content is organized across Databricks, standardizing around filesystem primitives that apply to every product surface

Job description

Databricks is seeking a Senior Software Engineer for the Files team to push the boundaries of storage and distributed systems. You will shape the filesystem strategy to power agentic workloads and data-centric platforms, collaborating across platform teams.

You will design and optimize a filesystem powering tens of thousands of workloads, with responsibilities spanning API to kernel, and contributing to compute/storage architecture evolutions.

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