10 hours ago
Base Salary
$250k - $300k/yr
Responsibilities
- Design and build distributed storage, caching, preloading, and peer-to-peer systems for Modal’s serverless platform.
- Improve cold-start performance by caching and sharing data across workers and datacenters.
- Own durability and cost at petabyte scale through replication, data migration, and garbage collection.
- Work across local disk, page cache, distributed blob storage, and garbage-collection systems.
- Participate in the on-call rotation and respond to production incidents.
- Develop systems for multi-provider replication, colocated storage clusters, and high-throughput customer workloads.
Requirements
- 5+ years of experience writing high-quality production code.
- Experience building high-performance distributed storage or caching systems at large scale.
- Strong cloud skills, including deep familiarity with object storage such as S3, CDNs, and their consistency, throughput, and cost characteristics.
- Strong knowledge of low-level operating-system foundations, including the Linux kernel, file systems, page cache, and containers.
- Willingness to participate in on-call rotations and respond to production incidents.
- Experience with replication, content addressing, and consistency models in multi-region or multi-cloud systems is preferred.
- Experience operating petabyte-scale storage systems, high-throughput read/write paths, large-scale garbage collection, or data migration is preferred.
- Experience with petabyte-scale data engineering is preferred.
- Prior experience with Rust is preferred.
About Modal
Modal builds a serverless compute platform for AI and data workloads, offering instant GPU access, sub-second container starts, and native storage to run inference, fine-tuning, and batch jobs. It sells a usage-based cloud service to developers and ML teams to deploy generative models and pipelines. Privately held and headquartered in New York City, its customers include companies like DoorDash and Ramp.