Member of Technical Staff - Lead, Storage

Mixpeek; San Francisco (CA); On-site; USD 260,000 - 380,000; Full time

Job summary

Modal is seeking a strong technical lead to guide engineers designing and maintaining a high-performance distributed storage system that underpins container images, volumes, and checkpoints. You will set architectural direction, own the roadmap, and balance durability and cost across multi-cloud deployments.

You will stay hands-on across the stack, overseeing garbage collection, replication, and performance optimizations while driving observability and on-call readiness.

Qualifications

• 7+ years of production-grade coding experience.
• 3+ years of people management, leading teams through planning and performance.
• Experience building high-performance distributed storage or caching at scale.
• Strong cloud skills with object storage, CDNs, and cost considerations.
• Deep knowledge of Linux kernel, file systems, page cache, containers.
• Experience with replication and consistency models in multi-region or multi-cloud deployments.
• Experience operating storage systems at petabyte scale and owning cost planning.
• Track record of setting technical direction and driving architectural decisions.
• Willingness to participate in on-call rotation and incidents.

Responsibilities

• Lead the team of engineers, guiding design and implementation of the serverless storage platform.
• Set technical direction for primitives used by other teams (filesystems, training, sandboxes).
• Own the roadmap from current hard problems to architectural bets on blobnet concepts.
• Balance durability, latency, throughput, and cost across systems and deployments.
• Manage a small team while staying hands-on across stack from disk to distributed storage.
• Improve observability, automation, and on-call practices to keep systems healthy.

Skills

Production-grade coding

People management

Distributed storage systems

Cloud storage & CDN

Linux kernel & OS

Replication & consistency

Petabyte-scale data

On-call responsiveness

Architecture direction

Tools

Rust

Job description

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