Engineering Manager, Runtime Fabric (Storage Products)
AI in this role
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 of the Runtime Fabric 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.
RESPONSIBILITIES
Team Leadership & Culture
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.
Technical Direction
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.
Cross-Functional Partnership
Act as the primary advocate for Runtime Fabric 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.
REQUIREMENTS
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.
Strong systems programming background in Go and/or C/C++.
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.
NICE TO HAVE
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.
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).
How we rate this
Engineering Manager, Runtime Fabric (Storage Products) at Baseten rates 92 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a research question you investigated. What did you find?
- Walk me through how you've used Cursor in your day-to-day work.
- What are the limits of Clay that you've run into, and how did you work around them?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: AI Research, Cursor, and Clay. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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