About Us:
AI needs a new infrastructure layer. We're building it at Modal.
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 Lovable, 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 recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
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.
The Role:
We are looking for a strong technical lead to guide the engineers designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. You'll lead the team responsible for 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 set technical direction for the primitives that other teams (filesystems, training, sandboxes) build on, balancing durability, latency, throughput, and cost. You'll own the roadmap from today's hardest problems (garbage collection at petabyte scale, active-active replication, rate limiting that protects the upstream without wasting utilization) to the architectural bets that decide what blobnet becomes: storage colocated with the GPUs, tiered writes, and capacity planning against provider limits. You'll manage a team of 3–8 engineers while staying hands-on across the stack, from local disk and page cache to distributed blob storage and garbage collection, and you'll guide the observability, automation, and on-call practices that keep the system healthy as it grows by orders of magnitude.
Requirements:
7+ years of experience writing high-quality production code
3+ years of direct people management experience, ideally leading a team of engineers through project planning, growth, and performance conversations
Experience building high-performance distributed storage or caching systems at a large scale (the more challenges you've worked through, the better)
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.)
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), including owning cost and capacity planning
Track record of setting technical direction and driving architectural decisions across a team, and of building the primitives other teams depend on
Willingness to step into the thick of it with our on-call rotation and respond to production incidents
Nice-to-Haves:
Experience with data engineering at petabyte-scale.
Prior experience with Rust
Key Things the Team Is Working On:
P2P sharing of data across workers within a single datacenter to dramatically reduce ingress
Replicating data across multiple blob storage providers
Automating garbage collection across hundreds of petabytes of data
Deploying colocated storage clusters to datacenters to accelerate high-throughput customer workloads