Engineering Manager, Runtime Fabric

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 Fabrics 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 of the Runtime Fabrics team, you will lead this work, setting technical direction, growing a world-class team of systems engineers, and ensuring the team's output shapes not just Baseten's infrastructure but the open-source container ecosystem at large. If you've contributed to containerd, runc, or related OCI projects and are ready to lead a team solving some of the hardest problems in infrastructure today, we'd love to talk.

- Recruit, hire, and develop a high-performing team of systems engineers with deep container and Linux expertise.

- Foster a culture of technical rigor, open-source contribution, and continuous improvement.

- Provide regular coaching, feedback, and career development support to your direct reports.

- Partner with engineering leadership to define the long-term vision and roadmap for container runtime and storage infrastructure.

- Guide the team in extending and hardening containerd, runc, and related OCI ecosystem projects to meet the GPU-specific requirements of production AI inference, including startup performance, GPU device access, and multi-tenant isolation.

- Oversee the architecture and evolution of the Baseten Delivery Network: the tiered caching and weight delivery system that makes cold starts 2-3x faster and eliminates thundering herd failures during burst scaling events.

- Drive the expansion of BDN's architecture, currently focused on model weights, to container images, training checkpoints, and deployment artifacts.

- Provide technical oversight on GPU-aware isolation mechanisms for multi-tenant inference, including secure container runtimes, Linux namespace hardening, and longer-term micro-VM integration.

- Ensure the team maintains end-to-end ownership of the container startup performance path, from snapshotter initialization through weight delivery to first inference request.

- Champion the team's contributions back to the open-source containerd ecosystem alongside a team of core maintainers.

- Act as the primary advocate for Runtime Fabrics across the organization, ensuring upstream and downstream teams have the integration support they need.

- Collaborate with product and engineering stakeholders to prioritize investments based on business impact and infrastructure reliability.

- Communicate team progress, technical trade-offs, and architectural decisions clearly to leadership.

- Proven experience managing and growing engineering teams in a systems, infrastructure, or low-level runtime context.

- Deep familiarity with the Linux container ecosystem: containerd, runc, OCI Runtime Spec, Linux namespaces, and cgroups, with the ability to engage credibly in code reviews and architectural discussions.

- Contributions to containerd/containerd, opencontainers/runc, google/gvisor, kata-containers/kata-containers, or closely related open-source projects.

- Experience with distributed storage systems, content-addressable storage, or large-scale caching infrastructure.

- 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.

- Experience with GPU device access in containers: NVIDIA Container Toolkit, CDI (Container Device Interface), or GPU-aware scheduling.

- Familiarity with lazy-loading snapshotters (stargz, soci, EROFS/Nydus) or peer-to-peer image distribution.

- Experience with secure container runtimes (gVisor, Sysbox) or micro-VM technologies (Firecracker, Cloud Hypervisor).

- Understanding of containerd's shim API (v2) and experience building custom shim implementations.

- Background in multi-tenant infrastructure or security-sensitive serving environments.