About this role
Designing caching, preloading, and peer-to-peer layers to hide object-store latency forms the core of daily engineering tasks. Engineers in this group build and maintain the distributed object storage system that underpins container images, volumes, and checkpoints across hundreds of petabytes of data. Responsibilities include handling streaming and batch replication, managing garbage collection over billions of objects, and optimizing performance from local disks and page caches to distributed blob storage.
Modal created this role to support a growing serverless platform that provides instant GPU access, sub-second container starts, and native storage for category-defining AI companies. Candidates who thrive here possess deep technical curiosity about low-level operating system foundations, distributed systems, and petabyte-scale storage challenges. Engineers joining the company help shape infrastructure for the artificial intelligence era, working alongside open-source creators and academic researchers.
What you will do
- Design and build caching, preloading, and peer-to-peer layers that hide object-store latency.
- Manage durability and cost at petabyte scale across hundreds of petabytes of data.
- Handle streaming and batch replication between origins as well as large-scale garbage collection.
- Work across the stack from local disk and page cache to distributed blob storage.
- Participate in the on-call rotation to respond to production incidents.
What you need
- Bring five or more years of experience writing high-quality production code.
- Demonstrate prior work building high-performance distributed storage or caching systems at scale.
- Possess strong cloud skills with deep familiarity with object storage like S3, CDNs, and consistency models.
- Show strong knowledge of low-level operating system foundations including the Linux kernel, file systems, page caches, and containers.
- Maintain a willingness to participate in the on-call rotation and handle production incidents.
Nice to have
- Experience with replication, content addressing, and consistency models in multi-region or multi-cloud systems
- Experience operating storage systems at scale including petabyte-scale datasets and large-scale garbage collection
- Experience with data engineering at petabyte-scale
- Prior experience with Rust
Skills and tech stack
Questions candidates ask
What kind of data scale does the storage team handle?
The distributed object storage system underpins container images, volumes, and checkpoints comprising hundreds of petabytes of data. This infrastructure manages billions of objects across multiple cloud object stores, CDNs, and local NVMe caches.
What specific projects will the storage engineer tackle?
The team focuses on peer-to-peer sharing of data across workers within a single datacenter to reduce ingress. Additional initiatives include replicating data across multiple blob storage providers, automating garbage collection, and deploying colocated storage clusters.
What are the core technical background expectations?
Applicants need five or more years of experience writing high-quality production code and building high-performance distributed storage or caching systems at scale. Strong cloud skills with object storage and deep knowledge of low-level operating system foundations are also required.