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 workloads. The company operates a serverless cloud platform serving category-defining customers like Ramp, Cognition, DoorDash, and Suno. Modal has achieved $1B annualized run rate (15x growth year-over-year) and recently raised a $750M Series D at a $15.75B valuation.
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, with local NVMe caching across a large fleet of workers in many datacenters and peer-to-peer sharing within each datacenter.
Key responsibilities include:
- Designing caching, preloading, and peer-to-peer layers to hide object-store latency and reduce cold-start times
- Owning durability and cost optimization at petabyte scale, including streaming and batch replication between origins and garbage collection over billions of objects
- Working across the full stack from local disk and page cache to distributed blob storage
- Participating in on-call rotation and responding to production incidents
- Shaping the future of storage architecture as Modal pushes storage closer to workloads
Current team priorities include: P2P data sharing across workers within datacenters to reduce ingress, replicating data across multiple blob storage providers, automating garbage collection across hundreds of petabytes, and deploying colocated storage clusters to datacenters for high-throughput customer workloads.
The team includes creators of popular open-source projects (Seaborn, Luigi), academic researchers, international olympiad medalists, and experienced engineering leaders.
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 with 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)
- Willingness to participate in 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 petabyte scale with high-throughput read/write paths and large-scale garbage collection or data migration
- Data engineering experience at petabyte scale
- Prior experience with Rust
About Modal Labs
AI / Data / Infrastructure — serverless cloud platform for AI, data, and compute-intensive applications.
