Baseten

Engineering Manager, Runtime Fabric

San Francisco

Sponsorship not specifiedDetected 43 days ago

Code ReviewLinuxMachine LearningSparkResearchLeadershipCommunicationMentoring

About the role

AI inference is not a general-purpose workload.
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.
That vertical ownership means we can fix these problems at the root.

Recruit, hire, and develop a high-performing team of systems engineers with deep container and Linux expertise.
Provide regular coaching, feedback, and career development support to your direct reports.
Drive the expansion of BDN's architecture, currently focused on model weights, to container images, training checkpoints, and deployment artifacts.
Ensure the team maintains end-to-end ownership of the container startup performance path, from snapshotter initialization through weight delivery to first inference request.
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.
We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital.
Join us and help build the platform engineers turn to to ship AI products.

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.
Experience with distributed storage systems, content-addressable storage, or large-scale caching infrastructure.
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).
Background in multi-tenant infrastructure or security-sensitive serving environments.

Competitive compensation, including meaningful equity.
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
Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.
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.
Partner with engineering leadership to define the long-term vision and roadmap for container runtime and storage infrastructure.
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.

This listing is sourced directly from Baseten's careers page and normalized into a canonical job model.