Engineering Manager

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. Baseten owns the entire pipeline, from the moment a developer pushes a model to the moment a request gets a response. The Runtime Fabric team is 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. 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.

• 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.
• 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.
• 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.

• Experience managing a team of engineers in a systems, infrastructure, or storage context, including hiring and performance management.
• 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.