Engineering Manager Runtime - Storage

San FranciscoEngineeringFullTime

Posted$165K to $330K · period not statedfirst seen Oct 15, 2026posted Oct 15, 2026

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archived Oct 15, 2026

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ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products.

THE ROLE

Container runtimes were designed for general-purpose software workloads. AI inference is not a general-purpose workload. Running large models at production scale exposes cracks in every layer of the container stack: runtimes unaware of GPU memory constraints, images that take minutes to pull when a model needs to scale to thousands of replicas, and isolation mechanisms that weren’t designed for the multi-tenant serving environments that production AI requires. The tools the industry has relied on for a decade weren’t built for this, and patching around those limitations at higher layers only goes so far. Baseten owns the entire pipeline, from the moment a developer pushes a model to the moment a request gets a response. That vertical ownership means we can fix these problems at the root. The Runtime Fabric team is doing exactly that: purpose-building the container runtime and storage layers for AI inference workloads, led by some of the world’s top containerd maintainers. As Engineering Manager for Storage Products on the Runtime Fabric team, you will lead the engineers who build and run the storage products behind that stack: the Baseten Delivery Network (BDN), which sits in the cold start path of every model on the platform, the snapshotters and filesystems that expose model weights and container images to a running container, and the storage that model deployment and training need next. The team is advancing the next generation of caching for inference workloads, from the origin tier to the GPU host. You will own reliability for the area as a measured outcome, report to the head of Runtime Fabric, and stay close to the code through design and code review. If you’ve managed systems engineers and have hands-on experience with caching, content-addressable storage, or distributed filesystems, we’d love to talk. EXAMPLE INITIATIVES How the Baseten Delivery Network (BDN) makes cold starts fast Extending BDN from model weights to container image layers, training checkpoints, and deployment artifacts Lazy-loading snapshotters and block-level delivery (EROFS, FUSE, ublk) so a container can start before the full image or model has arrived

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RESPONSIBILITIES

Team Leadership and Culture Hire and grow a team of 5 to 10 systems engineers with storage, caching, and Linux expertise Run the team’s planning and delivery: scope work against the Runtime Fabric roadmap, set priorities, and ship on a predictable cadence Provide regular coaching, feedback, and career development support to your direct reports Own on-call health for the team’s services, including escalation paths and follow-through on incidents Technical Direction Guide the architecture and evolution of the Baseten Delivery Network: the tiered caching and weight delivery product that makes cold starts 2-3x faster and eliminates thundering herd failures during burst scaling events Shape storage products tailored to Baseten’s workloads: extend BDN from model weights to container images, training checkpoints, and deployment artifacts, and define the interfaces that model deployment, training, and customers use to reach them Oversee the on-host side of delivery: lazy-loading snapshotters, EROFS and overlayfs layouts, and block-level delivery (FUSE, ublk) for multi-gigabyte files Own reliability of the delivery path as a measured outcome: end-to-end instrumentation from snapshotter initialization to first inference request, targets for cold start and cache behavior, tolerance for failing upstream registries and object stores, and safe rollout of changes to a path every customer depends on Make the delivery path work across heterogeneous clusters and cloud providers, each with its own object store and network Review designs and code across the area, and take on hands-on work where it unblocks the team Champion the team’s contributions back to containerd and the snapshotter ecosystem alongside the maintainers on the Runtime Fabric team Cross-Functional Partnership Act as the point of contact for storage across Baseten, ensuring model deployment, training, and platform teams have the integration support they need Optimize requirements for the underlying storage platforms with the infrastructure team: object storage, local NVMe and block storage, and network bandwidth per cluster, so that capacity and provider decisions account for what the delivery path needs Manage the team’s dependencies in both directions: sequence work that model deployment, training, and infrastructure teams block on, and negotiate interfaces and timelines with the teams and upstream projects the storage path depends on Work with the head of Runtime Fabric and product stakeholders to prioritize storage investments based on customer impact and infrastructure reliability Communicate team progress, technical trade-offs, and incidents clearly to the team and to leadership

REQUIREMENTS

Experience managing a team of engineers in a systems, infrastructure, or storage context, including hiring and performance management [TODO: confirm whether a tech lead stepping into management qualifies] Hands-on background in distributed storage systems, content-addressable storage, or large-scale caching infrastructure Strong systems programming background in Go, Rust, and/or C/C++, with the ability to engage credibly in code reviews and architectural discussions Working knowledge of the Linux storage stack: filesystems, the page cache, block devices, and mounts Understanding of how container images are structured, stored, and delivered at scale Strong written and verbal communication skills, with the ability to influence without authority across teams

NICE TO HAVE

Contributions to containerd/containerd, snapshotter projects (stargz, soci, nydus, overlaybd), distribution/distribution, or closely related open-source projects Familiarity with lazy-loading snapshotters or peer-to-peer image distribution Experience with EROFS, FUSE, ublk, or io_uring Experience building or operating CDN, object storage, or large-scale caching infrastructure Experience running storage services on Kubernetes

BENEFITS

Competitive compensation, including meaningful equity (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) Paid parental leave Fertility and family-building stipend through Carrot (U.S. only) Company-facilitated 401(k) Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).