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San Francisco, California, United States
Onsite
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We are looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our serverless platform.
Specifically, you’ll be working on the distributed object storage system that underpins every container image, volume, and checkpoint on Modal: 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.
You’ll make cold starts feel local when the data is hundreds of milliseconds away, designing the caching, preloading, and peer-to-peer layers that hide object-store latency and keep public ingress off saturated uplinks. You’ll own durability and cost at petabyte scale, from streaming and batch replication between origins to garbage collection over billions of objects. You’ll work across the stack, from local disk and page cache to distributed blob storage and garbage collection, and you’ll help shape what storage becomes next as we push storage closer to workloads.
Build P2P sharing of data across workers within a single datacenter to dramatically reduce ingress
Replicate data across multiple blob storage providers
Automate garbage collection across hundreds of petabytes of data
Deploy colocated storage clusters to datacenters to accelerate high-throughput customer workloads
You have 5+ years of experience writing high-quality production code
You’ve built high-performance distributed storage or caching systems at a large scale (the more challenges you’ve worked through, the better)
You have strong cloud skills, including deep familiarity with object storage (S3 or similar), CDNs, and their consistency, throughput, and cost characteristics
You have strong knowledge of low-level operating system foundations (Linux kernel, file systems, page cache, containers, etc.)
You’re willing to step into the thick of it with our on-call rotation and respond to production incidents
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
