Modal

<h2>About Us: AI needs a new infrastructure layer. We're building it at Modal.</h2>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 <a target="_blank" rel="noopener noreferrer ugc" href="https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal">Ramp</a>, 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.,<a target="_blank" rel="noopener noreferrer ugc" href="https://github.com/mwaskom/seaborn">Seaborn</a>,<a target="_blank" rel="noopener noreferrer ugc" href="https://github.com/spotify/luigi">Luigi</a>), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.<h2>The Role:</h2>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. <h2>Requirements:</h2><ul><li>5+ years of experience writing high-quality production code</li><li>Experience building high-performance distributed storage or caching systems at a large scale (the more challenges you've worked through, the better)</li><li>Strong cloud skills, including deep familiarity with object storage (S3 or similar), CDNs, and their consistency, throughput, and cost characteristics</li><li>Strong knowledge of low-level operating system foundations (Linux kernel, file systems, page cache, containers, etc.)</li><li>Willingness to step into the thick of it with our on-call rotation and respond to production incidents </li></ul><h2>Nice-to-Haves:</h2><ul><li>Experience with replication, content addressing, and consistency models in multi-region or multi-cloud systems</li><li>Experience operating storage systems at scale (petabyte-scale datasets, high-throughput read/write paths, large-scale garbage collection or data migration)</li><li>Experience with data engineering at petabyte-scale.</li><li>Prior experience with Rust </li></ul><h2>Key Things the Team Is Working On:</h2><ul><li>P2P sharing of data across workers within a single datacenter to dramatically reduce ingress</li><li>Replicating data across multiple blob storage providers</li><li>Automating garbage collection across hundreds of petabytes of data</li><li>Deploying colocated storage clusters to datacenters to accelerate high-throughput customer workloads</li></ul>

Member of Technical Staff - Storage

<h2>About Us: AI needs a new infrastructure layer. We're building it at Modal.</h2>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 <a target="_blank" rel="noopener noreferrer ugc" href="https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal">Ramp</a>, 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.,<a target="_blank" rel="noopener noreferrer ugc" href="https://github.com/mwaskom/seaborn">Seaborn</a>,<a target="_blank" rel="noopener noreferrer ugc" href="https://github.com/spotify/luigi">Luigi</a>), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.<h2>The Role:</h2>We’re looking for strong engineers with experience building software that developers love to work with. Our ideal candidate is someone with a demonstrated drive to build beautiful abstractions that enhance developer productivity. <h2>Requirements:</h2><ul><li>5+ years of experience developing high-quality Python, JS or Go applications or libraries where the code is used or interacted with by other developers</li><li>A strong code-as-product sense that manifests as a focus on developer ergonomics and productivity.</li><li>Knowledge of async programming and concurrency management/pitfalls</li><li>A high level of customer empathy, good communication skills, and an openness to working directly with our users to help solve their problems.</li><li>Ability to participate in on-call rotation and respond to production incidents.</li><li>Ability to work in-person in our New York or Stockholm office.</li><li>Any of the following would be a plus:<ul><li>Having built developer tools or SDKs</li><li>Experience maintaining open-source software</li><li>Good understanding of agentic development workflows</li><li>Familiarity with modern data / ML / AI tools and workflows</li></ul></li></ul><h2>Key Actions the Team Is Working On:</h2><ul><li>Improving developer and agent experience of our SDK and CLI</li><li>Developing new primitives to meet demand for entirely new areas that arise as a result of the ongoing industry transformation</li></ul>

Member of Technical Staff - SDK

<h2>About Us: AI needs a new infrastructure layer. We're building it at Modal.</h2>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 <a target="_blank" rel="noopener noreferrer ugc" href="https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal">Ramp</a>, 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.,<a target="_blank" rel="noopener noreferrer ugc" href="https://github.com/mwaskom/seaborn">Seaborn</a>,<a target="_blank" rel="noopener noreferrer ugc" href="https://github.com/spotify/luigi">Luigi</a>), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.<h2>The Role:</h2>Modal’s Inference Runtime team owns the container runtime stack used to run inference and training workloads across our fleet. We work at the boundary of Linux, containers, filesystems, storage, GPU drivers, and distributed systems.Our goal is to make demanding ML workloads start quickly, run efficiently, and remain securely isolated — whether they use a single GPU, hundreds of gigabytes of memory, or multiple GPUs connected with RDMA.We’re looking for a systems engineer who enjoys working deep in the stack. You’ll build production runtime infrastructure in Rust and Go, diagnose difficult Linux and performance problems, and help determine the architecture of Modal’s container platform. You’ll also work closely with maintainers of gVisor and contribute to the runtime itself when the right fix belongs upstream.<h2>The Work:</h2><ul><li>Make container startup, checkpoint, and restore dramatically faster for large inference and training workloads.</li><li>Build multi-GPU and accelerator-aware snapshotting, including efficient handling of GPU memory and RDMA-enabled workloads.</li><li>Design zero-copy and low-copy data paths between container memory, filesystems, storage, and Modal’s runtime.</li><li>Optimize large snapshot pipelines using techniques such as parallel uploads, direct I/O, incremental snapshots, and more efficient memory handling.</li><li>Improve container image and filesystem performance across EROFS, FUSE, page caches, overlay filesystems, and remote storage.</li><li>Extend our sandboxed runtime to support new GPUs, drivers, profiling tools, and device capabilities across NVIDIA and AMD hardware.</li><li>Debug complex failures involving system calls, virtual memory, process lifecycle, kernel behavior, GPU drivers, and container isolation.</li><li>Safely roll out runtime and kernel changes across a heterogeneous fleet using compatibility controls, scheduling constraints, feature flags, and observability.</li><li>Work across the runtime, scheduler, storage, and GPU infrastructure—and take ambiguous production problems from investigation through deployment.</li></ul><h2>What We’re Looking For:</h2><ul><li>Strong Linux systems knowledge, particularly processes, virtual memory, filesystems, system calls, scheduling, namespaces, cgroups, and signals.</li><li>Experience building or debugging container runtimes, sandboxes, Linux kernel, or similarly low-level infrastructure.</li><li>Strong programming ability in Rust, Go, or another systems language, with an interest in becoming productive in both Rust and Go.</li><li>Experience profiling and improving systems where memory movement, I/O, synchronization, or kernel interactions dominate performance.</li><li>Comfort debugging across abstraction boundaries, from application behavior down through runtimes, drivers, and the kernel.</li><li>An ability to turn loosely defined production problems into well-designed, reliable systems.</li><li>A desire to own important infrastructure and work closely with both internal teams and customers.</li></ul><h2>Relevant Experience:</h2>Any of the following would be helpful, but none is required:<ul><li>gVisor, <code>runsc</code>, <code>runc</code>, OCI runtimes, seccomp, or checkpoint/restore systems.</li><li>Linux kernel development, virtualization, sandboxing, kernel modules, or device proxying.</li><li>CUDA, ROCm, GPU drivers, accelerator virtualization, or GPU profiling.</li><li>RDMA and high-performance networking.</li><li>FUSE, EROFS, direct I/O, <code>mmap</code>, page-cache behavior, or storage engines.</li><li>Large-memory or multi-GPU inference and training systems.</li><li>Contributions to open-source systems software.</li></ul>Prior gVisor or machine-learning experience is not required. We care more about strong systems fundamentals, curiosity, and the ability to learn unfamiliar parts of the stack.<h2>Why This Role:</h2>The container runtime is directly on the critical path for inference performance. Improvements here can substantially reduce cold starts, increase token throughput, unlock new accelerator types, and make previously impractical workloads possible.This is an opportunity to work on unusually deep systems problems with immediate production impact. You’ll have room to shape the architecture, contribute to open-source runtime technology, and take ownership of foundational infrastructure used by every inference and training workload on Modal.

Member of Technical Staff - Inference Runtime

<h2>About Us: AI needs a new infrastructure layer. We're building it at Modal.</h2>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 <a target="_blank" rel="noopener noreferrer ugc" href="https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal">Ramp</a>, 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.,<a target="_blank" rel="noopener noreferrer ugc" href="https://github.com/mwaskom/seaborn">Seaborn</a>,<a target="_blank" rel="noopener noreferrer ugc" href="https://github.com/spotify/luigi">Luigi</a>), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.<h2>About the Data Team:</h2>We’re growing our Data team and are looking for our first few key hires to build self-serve data tools and drive business strategy in the right direction.The mission of the Modal Data team is to make it easy to track company goals, make evidence-backed decisions, and prioritize the right work. We do this via:<ul><li>Self-serve AI analytics tools (Hex, Snowflake)</li><li>Embedding with teams as a “data adviser”, providing strategic analysis and consulting</li></ul><h2>What You'll Do:</h2><ul><li>Contribute to building <a target="_blank" rel="noopener noreferrer ugc" href="https://modal.com/blog/analytics-stack">the most modern analytics stack</a> in Data today to support AI-driven self-serve analysis, key metrics tracking, and external customer reporting</li><li>Influence work on new products like <a target="_blank" rel="noopener noreferrer ugc" href="https://modal.com/blog/qwen3-8-2-4t-a95b-now-available-on-modal">LLM Inference Endpoints</a> through product analytics tracking</li><li>Identify millions of dollars of cost savings and optimization across our tools and financial operations</li><li>Write data pipelines that power the operations of our business, such as our cloud compute economics or sales comp</li><li>Create foundational datasets that can be used by people and AI tools to answer questions around product use cases, financial reporting, and marketing campaigns</li></ul><h2>What You Should Have:</h2><ul><li>SQL fluency, Python proficiency</li><li>Professional experience with at least 2 of the following tools: Snowflake, dbt, dlt, Modal, Hex, Posthog</li><li>Ability to extend their work beyond just data reporting and into action and impact</li><li>High attention to detail</li><li>Excellent and precise communicator</li><li>Strong personability and relationship building skills </li></ul><h2>Nice-to-Have:</h2><ul><li>Experience with AI products, especially LLM inference and sandboxes</li><li>Experience in fin ops, fraud, sales ops, or risk, especially in the context of AI (e.g. token cost optimization)</li><li>Project management skills</li><li>Strong presence in the data community online and offline</li></ul>

Analytics Engineer

<h2>About Us: AI needs a new infrastructure layer. We're building it at Modal.</h2>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 <a target="_blank" rel="noopener noreferrer ugc" href="https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal">Ramp</a>, 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.,<a target="_blank" rel="noopener noreferrer ugc" href="https://github.com/mwaskom/seaborn">Seaborn</a>,<a target="_blank" rel="noopener noreferrer ugc" href="https://github.com/spotify/luigi">Luigi</a>), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.<h2>The Role:</h2>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 Modal's machines layer: the fleet of bare metal and cloud hosts that every Function, Sandbox, and training job runs on, and the control plane that provisions, images, monitors, and repairs them. You'll automate the integration of new capacity from a growing set of hardware providers; from auditing and benchmarking hosts and clusters, to maintaining our machine images, configuring GPUs, RDMA, networking, and storage, and getting machines into production. You'll build the automation that keeps the fleet healthy without human intervention: detecting bad GPUs, thermals, and disks. You'll dig into whatever is between the hardware and the software that runs on top of it, whether that is a kernel panic, a broadcast storm during boot, or getting our container runtime to run on new architectures and platforms.<h2>Requirements:</h2><ul><li>5+ years of experience writing high-quality production code</li><li>Experience operating fleets of physical hardware (bare metal provisioning, BMC/IPMI, PXE or network boot, firmware) or building the control planes that manage them (the more challenges you've worked through, the better)</li><li>Strong cloud skills</li><li>Strong knowledge of low-level operating system foundations (Linux kernel, drivers, networking, file systems, containers, etc.)</li><li>Effective at debugging across layers, from BGP flapping, Linux RPS, and vBIOS bugs to a Python control-plane service</li><li>Willingness to step into the thick of it with our on-call rotation and respond to production incidents</li></ul><h2>Nice-to-Haves:</h2><ul><li>Experience with GPUs and the NVIDIA software stack in production (drivers, health monitoring, XIDs, RDMA/NVLink)</li><li>Prior experience with Go</li></ul><h2>Key Things the Team Is Working On:</h2><ul><li>Automatic remediation of unhealthy machines (power cycling, reimaging, GPU recovery) to maximize uptime and minimize operator toil.</li><li>Automatic integration of new CPU, GPU, and storage servers into the fleet while managing hardware and network heterogeneity.</li><li>Network health monitoring and reliability across many datacenters, and standardization of bare metal network configuration.</li><li>Automatic hardware acceptance testing and benchmarking (CPU, disk, GPU, interconnect, network).</li><li>Custom network bootloader, machine image pipeline, and kernel and firmware management across the fleet.</li></ul>

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Member of Technical Staff - Storage

Member of Technical Staff - SDK

Member of Technical Staff - Inference Runtime

Analytics Engineer

Member of Technical Staff - Machines

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