SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Modal is building a new infrastructure layer for AI—a serverless cloud platform for AI, data, and compute-intensive applications. The company recently raised $355M in Series C funding at a $4.65B valuation and has crossed $300M+ ARR. Customers include Lovable, Ramp, Cognition, DoorDash, and Suno.
You will join the storage team to design, build, and maintain the distributed object storage system that underpins every container image, volume, and checkpoint on Modal's platform. This system manages hundreds of petabytes of data replicated across multiple cloud object stores and a CDN, cached on local NVMe across a large fleet of workers in many datacenters, and shared peer-to-peer within each datacenter.
Key responsibilities include:
- Designing caching, preloading, and peer-to-peer layers that hide object-store latency and keep public ingress off saturated uplinks
- Owning durability and cost at petabyte scale, from streaming and batch replication between origins to garbage collection over billions of objects
- Working across the full stack, from local disk and page cache to distributed blob storage and garbage collection
- Participating in on-call rotation and responding to production incidents
- Shaping the future of storage as Modal pushes storage closer to workloads
Key projects the team is working on include P2P sharing of data across workers within a single datacenter, replicating data across multiple blob storage providers, automating garbage collection across hundreds of petabytes, and deploying colocated storage clusters to datacenters.
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 (S3 or similar), CDNs, and their consistency, throughput, and cost characteristics
- Strong knowledge of low-level operating system foundations (Linux kernel, file systems, page cache, containers, etc.)
- Willingness to step into on-call rotation and respond to production incidents
NICE-TO-HAVES:
- Experience with replication, content addressing, and consistency models in multi-region or multi-cloud systems
- Experience operating storage systems at scale (petabyte-scale datasets, high-throughput read/write paths, large-scale garbage collection or data migration)
- Experience with data engineering at petabyte-scale
- Prior experience with Rust
About Modal Labs
AI / Data / Infrastructure — serverless cloud platform for AI, data, and compute-intensive applications.
